{"id":1619,"date":"2020-02-06T02:20:33","date_gmt":"2020-02-06T02:20:33","guid":{"rendered":"http:\/\/www.marekrei.com\/blog\/?p=1619"},"modified":"2020-02-23T21:34:43","modified_gmt":"2020-02-23T21:34:43","slug":"ml-and-nlp-publications-in-2019","status":"publish","type":"post","link":"https:\/\/www.marekrei.com\/blog\/ml-and-nlp-publications-in-2019\/","title":{"rendered":"ML and NLP Publications in 2019"},"content":{"rendered":"<p><script>\nfunction scaleBarGraph(data, limit) {\n  for (i = 0; i < data.length; i++){\n    data[i].x = data[i].x.slice(0,limit);\n    data[i].y = data[i].y.slice(0,limit);\n  }\n}\nfunction scaleLineGraph(data, limit) {\n  data.splice(limit,1000);\n}\nvar badkeys = [\"Yang Liu\"]; \/\/ As far as I can tell, there are two or more researchers publishing under this name. So removing it from the list.\nfunction fixBarGraph(data){\n  for (var i = 0; i < data.length; i++){\n    for (var j = 0; j < badkeys.length; j++){ var ix = data[i].x.indexOf(badkeys[j]); if (ix >= 0){\n        data[i].x.splice(ix, 1);\n        data[i].y.splice(ix, 1);\n      }\n    }\n  }\n}\nfunction fixLineGraph(data){\n  for (var j = 0; j < badkeys.length; j++){\n    for (var i = 0; i < data.length; i++){\n      if (data[i].text == badkeys[j]){\n        data.splice(i, 1);\n        break;\n      }\n    }\n  }\n}\n<\/script><\/p>\n<p>It is about time we once again take a look at the publication statistics of the past year. 2019 was another record breaking year in machine learning and NLP research. Nearly all conferences had more attendees and more publications than ever before. For example, NeurIPS had <a href=\"https:\/\/medium.com\/syncedreview\/neurips-2019-the-numbers-c1808fba9480\">6,743 submissions and 1,428 accepted papers<\/a>, which eclipses all the previous iterations. Because the conference sold out so fast last year, the organizers had to implement a <a href=\"https:\/\/nips.cc\/Lottery\">randomised lottery<\/a> for the tickets this time.<\/p>\n<p>In this post you will find a number of graphs to illustrate the publication patterns from 2019.<br \/>\n I have included the following conferences and journals: ACL, EMNLP, NAACL, EACL, COLING, TACL, CL, CoNLL, NeurIPS, ICML, ICLR, AAAI. The paper information is crawled and processed automatically from the corresponding proceedings. While names and titles are quite easily accessible, getting the author affiliations is the noisiest part of the process, as this needs to be extracted directly from the pdf files. I've kept improving the pipeline every year so it should be more accurate than any of the previous iterations. If you do spot some mistakes, let me know.<\/p>\n<p>The analysis this year includes some brand new statistics and graphs. This is thanks to <a href=\"https:\/\/pfeiffer.ai\/\">Jonas Pfeiffer<\/a> (<a href=\"https:\/\/twitter.com\/PfeiffJo\">@PfeiffJo<\/a>) and <a href=\"https:\/\/www.cl.cam.ac.uk\/~apc38\/\">Andrew Caines<\/a> (<a href=\"https:\/\/twitter.com\/cainesap\">@cainesap<\/a>) who annotated the extracted organization names with origin countries. Andrew started the process with a subset of 2018 conferences for our joint article on <a href=\"https:\/\/www.marekrei.com\/blog\/geographic-diversity-of-nlp-conferences\/\">The Geographic Diversity of NLP Conferences<\/a>. Jonas then scaled this up to cover all of the organizations in all the conferences in this analysis. This allows us to create some new country-specific visualisations, which you'll see below.<\/p>\n<p>While this post highlights authors and organizations who have published the most in the past year, I want to emphasize that publication quantity should not be used as the main metric for good research. It's always better to have one really groundbreaking and influential piece of work over 10 forgettable and incremental papers. These statistics are just meant to give an informative overview of the most active publication centres in this field and hopefully inspire some new researchers to publish their own ideas.<\/p>\n<h2>Venues<\/h2>\n<p>Essentially all conferences had record-breaking numbers of publications in 2019. The journals have a fairly stable publication rate, COLING and EACL didn't happen this year, and every other conference just grew substantially. NeurIPS is by far the biggest conference and it now has a lead of nearly 300 papers over AAAI.<\/p>\n<div id=\"myplot_graph2019_all_source_year\"><\/div>\n<p><script>\nvar trace_graph2019_all_source_year_0 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [187, 328, 286, 318, 328, 302, 381, 660, ],\n  text: \"ACL\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_source_year_1 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [139, 205, 226, 312, 264, 323, 549, 681, ],\n  text: \"EMNLP\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_source_year_2 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [97, 140, 0, 186, 181, 0, 357, 451, ],\n  text: \"NAACL\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_source_year_3 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [85, 0, 124, 0, 0, 239, 0, 0, ],\n  text: \"EACL\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_source_year_4 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [332, 0, 221, 0, 337, 0, 330, 0, ],\n  text: \"COLING\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_source_year_5 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [0, 25, 20, 38, 31, 45, 56, 97, ],\n  text: \"CoNLL\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_source_year_6 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [0, 35, 43, 42, 39, 37, 48, 45, ],\n  text: \"TACL\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_source_year_7 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [36, 35, 35, 33, 31, 28, 34, 18, ],\n  text: \"CL\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_source_year_8 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [368, 360, 411, 403, 569, 679, 1009, 1427, ],\n  text: \"NeurIPS\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_source_year_9 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [243, 283, 310, 270, 322, 434, 621, 773, ],\n  text: \"ICML\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_source_year_10 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [0, 23, 34, 42, 80, 198, 336, 502, ],\n  text: \"ICLR\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_source_year_11 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [311, 202, 398, 538, 548, 639, 937, 1147, ],\n  text: \"AAAI\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar data_graph2019_all_source_year = [trace_graph2019_all_source_year_0, trace_graph2019_all_source_year_1, trace_graph2019_all_source_year_2, trace_graph2019_all_source_year_3, trace_graph2019_all_source_year_4, trace_graph2019_all_source_year_5, trace_graph2019_all_source_year_6, trace_graph2019_all_source_year_7, trace_graph2019_all_source_year_8, trace_graph2019_all_source_year_9, trace_graph2019_all_source_year_10, trace_graph2019_all_source_year_11];\nPlotly.newPlot('myplot_graph2019_all_source_year', data_graph2019_all_source_year, {margin: { l: 30, r: 0, b: 100, t: 0, pad: 4 }, showlegend: false, showticklabels: true, xaxis:{autotick: false}});\n<\/script><\/p>\n<h2>Organizations<\/h2>\n<p>Let's see which organizations published the most in 2019. Google has taken a comfortable lead, publishing a substantial number of papers in every venue. They had more than twice as many papers at ICML as the next closest organization (MIT). Worth noting that in previous years I included DeepMind papers also under Google, whereas this time DeepMind is separated as its own entity. Microsoft and CMU are also publishing an impressive amount of research.<\/p>\n<div id=\"myplot_graph2019_last_org_source\" style=\"height:550px;\"><\/div>\n<p><script>\nvar trace_graph2019_last_org_source_0 = {  x: [\"Google\", \"Microsoft\", \"Carnegie Mellon University\", \"Massachusetts Institute of Technology\", \"Stanford University\", \"Facebook\", \"Tsinghua University\", \"IBM\", \"Peking University\", \"UC Berkeley\", \"DeepMind\", \"University of Washington\", \"Amazon\", \"University of Cambridge\", \"University of Oxford\", \"University of Chinese Academy of Sciences\", \"Chinese Academy of Sciences\", \"University of California Los Angeles\", \"University of Illinois\", \"Allen Institute for Artificial Intelligence\", \"University of Edinburgh\", \"Cornell University\", \"University of Toronto\", \"University of Southern California\", \"Princeton University\", \"Harvard University\", \"Johns Hopkins University\", \"Columbia University\", \"Nanjing University\", \"The Chinese University of Hong Kong\", \"Georgia Institute of Technology\", \"University of Texas at Austin\", \"New York University\", \"Universite\u0301 de Montre\u0301al\", \"Duke University\", \"Zhejiang University\", \"University of Tokyo\", \"University of Science and Technology of China\", \"University of Amsterdam\", \"Beihang University\", \"Baidu\", \"ETH Z\u00fcrich\", \"Ecole Polytechnique Fe\u0301de\u0301rale de Lausanne\", \"INRIA\", \"Fudan University\", ],\n  y: [23, 57, 37, 21, 16, 9, 60, 33, 40, 16, 8, 7, 7, 6, 15, 43, 42, 15, 11, 3, 5, 10, 12, 18, 1, 6, 6, 3, 36, 22, 6, 10, 1, 4, 8, 24, 13, 31, 5, 30, 16, 7, 5, 2, 25, ],\n  name: 'AAAI',\n  type: 'bar'\n};\nvar trace_graph2019_last_org_source_1 = {  x: [\"Google\", \"Microsoft\", \"Carnegie Mellon University\", \"Massachusetts Institute of Technology\", \"Stanford University\", \"Facebook\", \"Tsinghua University\", \"IBM\", \"Peking University\", \"UC Berkeley\", \"DeepMind\", \"University of Washington\", \"Amazon\", \"University of Cambridge\", \"University of Oxford\", \"University of Chinese Academy of Sciences\", \"Chinese Academy of Sciences\", \"University of California Los Angeles\", \"University of Illinois\", \"Allen Institute for Artificial Intelligence\", \"University of Edinburgh\", \"Cornell University\", \"University of Toronto\", \"University of Southern California\", \"Princeton University\", \"Harvard University\", \"Johns Hopkins University\", \"Columbia University\", \"Nanjing University\", \"The Chinese University of Hong Kong\", \"Georgia Institute of Technology\", \"University of Texas at Austin\", \"New York University\", \"Universite\u0301 de Montre\u0301al\", \"Duke University\", \"Zhejiang University\", \"University of Tokyo\", \"University of Science and Technology of China\", \"University of Amsterdam\", \"Beihang University\", \"Baidu\", \"ETH Z\u00fcrich\", \"Ecole Polytechnique Fe\u0301de\u0301rale de Lausanne\", \"INRIA\", \"Fudan University\", ],\n  y: [61, 30, 32, 42, 21, 28, 12, 14, 10, 27, 38, 7, 3, 12, 16, 2, 2, 17, 12, 3, 5, 3, 18, 4, 11, 4, 2, 3, 1, 5, 13, 5, 12, 13, 7, 2, 5, 4, 8, 0, 5, 9, 4, 4, 1, ],\n  name: 'ICLR',\n  type: 'bar'\n};\nvar trace_graph2019_last_org_source_2 = {  x: [\"Google\", \"Microsoft\", \"Carnegie Mellon University\", \"Massachusetts Institute of Technology\", \"Stanford University\", \"Facebook\", \"Tsinghua University\", \"IBM\", \"Peking University\", \"UC Berkeley\", \"DeepMind\", \"University of Washington\", \"Amazon\", \"University of Cambridge\", \"University of Oxford\", \"University of Chinese Academy of Sciences\", \"Chinese Academy of Sciences\", \"University of California Los Angeles\", \"University of Illinois\", \"Allen Institute for Artificial Intelligence\", \"University of Edinburgh\", \"Cornell University\", \"University of Toronto\", \"University of Southern California\", \"Princeton University\", \"Harvard University\", \"Johns Hopkins University\", \"Columbia University\", \"Nanjing University\", \"The Chinese University of Hong Kong\", \"Georgia Institute of Technology\", \"University of Texas at Austin\", \"New York University\", \"Universite\u0301 de Montre\u0301al\", \"Duke University\", \"Zhejiang University\", \"University of Tokyo\", \"University of Science and Technology of China\", \"University of Amsterdam\", \"Beihang University\", \"Baidu\", \"ETH Z\u00fcrich\", \"Ecole Polytechnique Fe\u0301de\u0301rale de Lausanne\", \"INRIA\", \"Fudan University\", ],\n  y: [93, 31, 29, 40, 31, 25, 11, 20, 9, 35, 24, 14, 7, 10, 21, 1, 4, 10, 8, 1, 7, 14, 10, 6, 19, 14, 8, 7, 5, 4, 12, 12, 7, 9, 11, 2, 7, 0, 4, 0, 0, 11, 13, 13, 3, ],\n  name: 'ICML',\n  type: 'bar'\n};\nvar trace_graph2019_last_org_source_3 = {  x: [\"Google\", \"Microsoft\", \"Carnegie Mellon University\", \"Massachusetts Institute of Technology\", \"Stanford University\", \"Facebook\", \"Tsinghua University\", \"IBM\", \"Peking University\", \"UC Berkeley\", \"DeepMind\", \"University of Washington\", \"Amazon\", \"University of Cambridge\", \"University of Oxford\", \"University of Chinese Academy of Sciences\", \"Chinese Academy of Sciences\", \"University of California Los Angeles\", \"University of Illinois\", \"Allen Institute for Artificial Intelligence\", \"University of Edinburgh\", \"Cornell University\", \"University of Toronto\", \"University of Southern California\", \"Princeton University\", \"Harvard University\", \"Johns Hopkins University\", \"Columbia University\", \"Nanjing University\", \"The Chinese University of Hong Kong\", \"Georgia Institute of Technology\", \"University of Texas at Austin\", \"New York University\", \"Universite\u0301 de Montre\u0301al\", \"Duke University\", \"Zhejiang University\", \"University of Tokyo\", \"University of Science and Technology of China\", \"University of Amsterdam\", \"Beihang University\", \"Baidu\", \"ETH Z\u00fcrich\", \"Ecole Polytechnique Fe\u0301de\u0301rale de Lausanne\", \"INRIA\", \"Fudan University\", ],\n  y: [111, 76, 82, 82, 79, 46, 34, 31, 25, 56, 47, 29, 22, 23, 36, 14, 7, 29, 28, 6, 9, 27, 25, 12, 37, 24, 8, 43, 11, 8, 24, 22, 25, 19, 23, 9, 14, 6, 11, 2, 9, 22, 27, 32, 7, ],\n  name: 'NeurIPS',\n  type: 'bar'\n};\nvar trace_graph2019_last_org_source_4 = {  x: [\"Google\", \"Microsoft\", \"Carnegie Mellon University\", \"Massachusetts Institute of Technology\", \"Stanford University\", \"Facebook\", \"Tsinghua University\", \"IBM\", \"Peking University\", \"UC Berkeley\", \"DeepMind\", \"University of Washington\", \"Amazon\", \"University of Cambridge\", \"University of Oxford\", \"University of Chinese Academy of Sciences\", \"Chinese Academy of Sciences\", \"University of California Los Angeles\", \"University of Illinois\", \"Allen Institute for Artificial Intelligence\", \"University of Edinburgh\", \"Cornell University\", \"University of Toronto\", \"University of Southern California\", \"Princeton University\", \"Harvard University\", \"Johns Hopkins University\", \"Columbia University\", \"Nanjing University\", \"The Chinese University of Hong Kong\", \"Georgia Institute of Technology\", \"University of Texas at Austin\", \"New York University\", \"Universite\u0301 de Montre\u0301al\", \"Duke University\", \"Zhejiang University\", \"University of Tokyo\", \"University of Science and Technology of China\", \"University of Amsterdam\", \"Beihang University\", \"Baidu\", \"ETH Z\u00fcrich\", \"Ecole Polytechnique Fe\u0301de\u0301rale de Lausanne\", \"INRIA\", \"Fudan University\", ],\n  y: [1, 2, 1, 0, 1, 1, 0, 2, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 2, 0, 0, 1, 0, 0, 0, 0, 0, ],\n  name: 'CL',\n  type: 'bar'\n};\nvar trace_graph2019_last_org_source_5 = {  x: [\"Google\", \"Microsoft\", \"Carnegie Mellon University\", \"Massachusetts Institute of Technology\", \"Stanford University\", \"Facebook\", \"Tsinghua University\", \"IBM\", \"Peking University\", \"UC Berkeley\", \"DeepMind\", \"University of Washington\", \"Amazon\", \"University of Cambridge\", \"University of Oxford\", \"University of Chinese Academy of Sciences\", \"Chinese Academy of Sciences\", \"University of California Los Angeles\", \"University of Illinois\", \"Allen Institute for Artificial Intelligence\", \"University of Edinburgh\", \"Cornell University\", \"University of Toronto\", \"University of Southern California\", \"Princeton University\", \"Harvard University\", \"Johns Hopkins University\", \"Columbia University\", \"Nanjing University\", \"The Chinese University of Hong Kong\", \"Georgia Institute of Technology\", \"University of Texas at Austin\", \"New York University\", \"Universite\u0301 de Montre\u0301al\", \"Duke University\", \"Zhejiang University\", \"University of Tokyo\", \"University of Science and Technology of China\", \"University of Amsterdam\", \"Beihang University\", \"Baidu\", \"ETH Z\u00fcrich\", \"Ecole Polytechnique Fe\u0301de\u0301rale de Lausanne\", \"INRIA\", \"Fudan University\", ],\n  y: [2, 1, 1, 2, 2, 4, 0, 3, 1, 0, 0, 2, 0, 0, 1, 1, 0, 1, 0, 3, 4, 1, 0, 0, 0, 1, 3, 0, 0, 1, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ],\n  name: 'TACL',\n  type: 'bar'\n};\nvar trace_graph2019_last_org_source_6 = {  x: [\"Google\", \"Microsoft\", \"Carnegie Mellon University\", \"Massachusetts Institute of Technology\", \"Stanford University\", \"Facebook\", \"Tsinghua University\", \"IBM\", \"Peking University\", \"UC Berkeley\", \"DeepMind\", \"University of Washington\", \"Amazon\", \"University of Cambridge\", \"University of Oxford\", \"University of Chinese Academy of Sciences\", \"Chinese Academy of Sciences\", \"University of California Los Angeles\", \"University of Illinois\", \"Allen Institute for Artificial Intelligence\", \"University of Edinburgh\", \"Cornell University\", \"University of Toronto\", \"University of Southern California\", \"Princeton University\", \"Harvard University\", \"Johns Hopkins University\", \"Columbia University\", \"Nanjing University\", \"The Chinese University of Hong Kong\", \"Georgia Institute of Technology\", \"University of Texas at Austin\", \"New York University\", \"Universite\u0301 de Montre\u0301al\", \"Duke University\", \"Zhejiang University\", \"University of Tokyo\", \"University of Science and Technology of China\", \"University of Amsterdam\", \"Beihang University\", \"Baidu\", \"ETH Z\u00fcrich\", \"Ecole Polytechnique Fe\u0301de\u0301rale de Lausanne\", \"INRIA\", \"Fudan University\", ],\n  y: [6, 2, 5, 2, 3, 1, 0, 1, 2, 0, 1, 4, 1, 2, 0, 4, 5, 2, 2, 4, 0, 2, 2, 5, 1, 0, 3, 0, 0, 0, 0, 1, 0, 0, 0, 0, 2, 0, 1, 0, 0, 1, 1, 0, 0, ],\n  name: 'CoNLL',\n  type: 'bar'\n};\nvar trace_graph2019_last_org_source_7 = {  x: [\"Google\", \"Microsoft\", \"Carnegie Mellon University\", \"Massachusetts Institute of Technology\", \"Stanford University\", \"Facebook\", \"Tsinghua University\", \"IBM\", \"Peking University\", \"UC Berkeley\", \"DeepMind\", \"University of Washington\", \"Amazon\", \"University of Cambridge\", \"University of Oxford\", \"University of Chinese Academy of Sciences\", \"Chinese Academy of Sciences\", \"University of California Los Angeles\", \"University of Illinois\", \"Allen Institute for Artificial Intelligence\", \"University of Edinburgh\", \"Cornell University\", \"University of Toronto\", \"University of Southern California\", \"Princeton University\", \"Harvard University\", \"Johns Hopkins University\", \"Columbia University\", \"Nanjing University\", \"The Chinese University of Hong Kong\", \"Georgia Institute of Technology\", \"University of Texas at Austin\", \"New York University\", \"Universite\u0301 de Montre\u0301al\", \"Duke University\", \"Zhejiang University\", \"University of Tokyo\", \"University of Science and Technology of China\", \"University of Amsterdam\", \"Beihang University\", \"Baidu\", \"ETH Z\u00fcrich\", \"Ecole Polytechnique Fe\u0301de\u0301rale de Lausanne\", \"INRIA\", \"Fudan University\", ],\n  y: [21, 25, 31, 12, 11, 14, 7, 18, 6, 3, 2, 19, 22, 22, 3, 1, 2, 3, 5, 25, 13, 4, 4, 7, 0, 12, 15, 5, 3, 7, 3, 3, 3, 3, 3, 5, 6, 0, 6, 2, 3, 2, 1, 0, 3, ],\n  name: 'NAACL',\n  type: 'bar'\n};\nvar trace_graph2019_last_org_source_8 = {  x: [\"Google\", \"Microsoft\", \"Carnegie Mellon University\", \"Massachusetts Institute of Technology\", \"Stanford University\", \"Facebook\", \"Tsinghua University\", \"IBM\", \"Peking University\", \"UC Berkeley\", \"DeepMind\", \"University of Washington\", \"Amazon\", \"University of Cambridge\", \"University of Oxford\", \"University of Chinese Academy of Sciences\", \"Chinese Academy of Sciences\", \"University of California Los Angeles\", \"University of Illinois\", \"Allen Institute for Artificial Intelligence\", \"University of Edinburgh\", \"Cornell University\", \"University of Toronto\", \"University of Southern California\", \"Princeton University\", \"Harvard University\", \"Johns Hopkins University\", \"Columbia University\", \"Nanjing University\", \"The Chinese University of Hong Kong\", \"Georgia Institute of Technology\", \"University of Texas at Austin\", \"New York University\", \"Universite\u0301 de Montre\u0301al\", \"Duke University\", \"Zhejiang University\", \"University of Tokyo\", \"University of Science and Technology of China\", \"University of Amsterdam\", \"Beihang University\", \"Baidu\", \"ETH Z\u00fcrich\", \"Ecole Polytechnique Fe\u0301de\u0301rale de Lausanne\", \"INRIA\", \"Fudan University\", ],\n  y: [27, 47, 34, 9, 11, 25, 22, 19, 31, 3, 6, 22, 24, 14, 4, 19, 20, 8, 16, 29, 21, 9, 2, 16, 1, 5, 12, 7, 5, 10, 3, 4, 5, 4, 2, 10, 3, 6, 8, 14, 10, 0, 0, 0, 5, ],\n  name: 'EMNLP',\n  type: 'bar'\n};\nvar trace_graph2019_last_org_source_9 = {  x: [\"Google\", \"Microsoft\", \"Carnegie Mellon University\", \"Massachusetts Institute of Technology\", \"Stanford University\", \"Facebook\", \"Tsinghua University\", \"IBM\", \"Peking University\", \"UC Berkeley\", \"DeepMind\", \"University of Washington\", \"Amazon\", \"University of Cambridge\", \"University of Oxford\", \"University of Chinese Academy of Sciences\", \"Chinese Academy of Sciences\", \"University of California Los Angeles\", \"University of Illinois\", \"Allen Institute for Artificial Intelligence\", \"University of Edinburgh\", \"Cornell University\", \"University of Toronto\", \"University of Southern California\", \"Princeton University\", \"Harvard University\", \"Johns Hopkins University\", \"Columbia University\", \"Nanjing University\", \"The Chinese University of Hong Kong\", \"Georgia Institute of Technology\", \"University of Texas at Austin\", \"New York University\", \"Universite\u0301 de Montre\u0301al\", \"Duke University\", \"Zhejiang University\", \"University of Tokyo\", \"University of Science and Technology of China\", \"University of Amsterdam\", \"Beihang University\", \"Baidu\", \"ETH Z\u00fcrich\", \"Ecole Polytechnique Fe\u0301de\u0301rale de Lausanne\", \"INRIA\", \"Fudan University\", ],\n  y: [36, 41, 36, 10, 9, 24, 22, 19, 26, 6, 3, 22, 20, 14, 3, 14, 15, 4, 7, 15, 20, 6, 3, 5, 1, 4, 13, 1, 5, 7, 1, 3, 5, 5, 4, 5, 4, 7, 11, 4, 10, 1, 2, 2, 8, ],\n  name: 'ACL',\n  type: 'bar'\n};\nvar data_graph2019_last_org_source = [trace_graph2019_last_org_source_0, trace_graph2019_last_org_source_1, trace_graph2019_last_org_source_2, trace_graph2019_last_org_source_3, trace_graph2019_last_org_source_4, trace_graph2019_last_org_source_5, trace_graph2019_last_org_source_6, trace_graph2019_last_org_source_7, trace_graph2019_last_org_source_8, trace_graph2019_last_org_source_9];\nfixBarGraph(data_graph2019_last_org_source);\nscaleBarGraph(data_graph2019_last_org_source, 32);\nPlotly.newPlot('myplot_graph2019_last_org_source', data_graph2019_last_org_source, {barmode: 'stack', margin: { l: 30, r: 0, b: 270, t: 20, pad: 4 }});\n<\/script><\/p>\n<p>Next up, we can see the overall stats between 2012-2019. While Google is dominating the previous year, CMU and Microsoft are still ahead in the marathon. Notably, the counts for CMU and Microsoft came out identical(!), with 1,215 papers from both organizations. After them, the main heavy hitters are Google, Stanford, MIT, IBM, Berkeley and Tsinghua.<\/p>\n<div id=\"myplot_graph2019_all_org_source\" style=\"height:550px;\"><\/div>\n<p><script>\nvar trace_graph2019_all_org_source_0 = {  x: [\"Carnegie Mellon University\", \"Microsoft\", \"Google\", \"Stanford University\", \"Massachusetts Institute of Technology\", \"IBM\", \"UC Berkeley\", \"Tsinghua University\", \"Peking University\", \"University of Washington\", \"Facebook\", \"University of Cambridge\", \"Chinese Academy of Sciences\", \"DeepMind\", \"University of Edinburgh\", \"University of Oxford\", \"University of Texas at Austin\", \"Columbia University\", \"Johns Hopkins University\", \"Cornell University\", \"University of Illinois\", \"University of Toronto\", \"INRIA\", \"Princeton University\", \"University of Pennsylvania\", \"University of Southern California\", \"Harvard University\", \"Georgia Institute of Technology\", \"University of Chinese Academy of Sciences\", 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trace_graph2019_all_org_source_5, trace_graph2019_all_org_source_6, trace_graph2019_all_org_source_7, trace_graph2019_all_org_source_8, trace_graph2019_all_org_source_9, trace_graph2019_all_org_source_10, trace_graph2019_all_org_source_11];\nfixBarGraph(data_graph2019_all_org_source);\nscaleBarGraph(data_graph2019_all_org_source, 32);\nPlotly.newPlot('myplot_graph2019_all_org_source', data_graph2019_all_org_source, {barmode: 'stack', margin: { l: 30, r: 0, b: 270, t: 20, pad: 4 }});\n<\/script><\/p>\n<p>If we look at the separation in time, we see that Google is sort of a late bloomer. 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California Los Angeles\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_org_year_35 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [25, 22, 20, 16, 22, 26, 50, 46, ],\n  text: \"University College London\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_org_year_36 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [30, 27, 21, 18, 22, 18, 42, 46, ],\n  text: \"National University of Singapore\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_org_year_37 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [14, 16, 19, 10, 19, 32, 58, 53, ],\n  text: \"Ecole Polytechnique Fe\u0301de\u0301rale de Lausanne\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_org_year_38 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [10, 10, 19, 20, 25, 34, 39, 60, ],\n  text: \"New York University\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_org_year_39 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [5, 14, 24, 20, 31, 33, 28, 58, ],\n  text: \"Universite\u0301 de Montre\u0301al\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_org_year_40 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [16, 20, 33, 23, 21, 20, 36, 42, ],\n  text: \"Harbin Institute of Technology\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_org_year_41 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [12, 4, 19, 10, 21, 20, 66, 49, ],\n  text: \"Shanghai Jiao Tong University\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_org_year_42 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [22, 7, 16, 15, 15, 30, 37, 56, ],\n  text: \"University of Tokyo\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_org_year_43 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [7, 4, 22, 18, 31, 25, 36, 54, ],\n  text: \"University of Amsterdam\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_org_year_44 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [1, 2, 5, 9, 19, 20, 50, 89, ],\n  text: \"Allen Institute for Artificial Intelligence\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar data_graph2019_all_org_year = [trace_graph2019_all_org_year_0, trace_graph2019_all_org_year_1, trace_graph2019_all_org_year_2, trace_graph2019_all_org_year_3, trace_graph2019_all_org_year_4, trace_graph2019_all_org_year_5, trace_graph2019_all_org_year_6, trace_graph2019_all_org_year_7, trace_graph2019_all_org_year_8, trace_graph2019_all_org_year_9, trace_graph2019_all_org_year_10, trace_graph2019_all_org_year_11, trace_graph2019_all_org_year_12, trace_graph2019_all_org_year_13, trace_graph2019_all_org_year_14, trace_graph2019_all_org_year_15, trace_graph2019_all_org_year_16, trace_graph2019_all_org_year_17, trace_graph2019_all_org_year_18, trace_graph2019_all_org_year_19, trace_graph2019_all_org_year_20, trace_graph2019_all_org_year_21, trace_graph2019_all_org_year_22, trace_graph2019_all_org_year_23, trace_graph2019_all_org_year_24, trace_graph2019_all_org_year_25, trace_graph2019_all_org_year_26, trace_graph2019_all_org_year_27, trace_graph2019_all_org_year_28, trace_graph2019_all_org_year_29, trace_graph2019_all_org_year_30, trace_graph2019_all_org_year_31, trace_graph2019_all_org_year_32, trace_graph2019_all_org_year_33, trace_graph2019_all_org_year_34, trace_graph2019_all_org_year_35, trace_graph2019_all_org_year_36, trace_graph2019_all_org_year_37, trace_graph2019_all_org_year_38, trace_graph2019_all_org_year_39, trace_graph2019_all_org_year_40, trace_graph2019_all_org_year_41, trace_graph2019_all_org_year_42, trace_graph2019_all_org_year_43, trace_graph2019_all_org_year_44];\nfixLineGraph(data_graph2019_all_org_year);\nscaleLineGraph(data_graph2019_all_org_year, 16);\nPlotly.newPlot('myplot_graph2019_all_org_year', data_graph2019_all_org_year, {margin: { l: 30, r: 0, b: 100, t: 0, pad: 4 }, showlegend: false, showticklabels: true, xaxis:{autotick: false}});\n<\/script><\/p>\n<h2>Authors<\/h2>\n<p>Looking at individual authors, <a href=\"http:\/\/rail.eecs.berkeley.edu\/\">Sergey Levine<\/a> (Berkeley) published an impressive 33 papers in 2019 - 12 in NeurIPS, 6 in ICML and 15 in ICLR. The other top authors are <a href=\"http:\/\/www.phontron.com\/\">Graham Neubig<\/a> (CMU), <a href=\"https:\/\/mila.quebec\/en\/yoshua-bengio\/\">Yoshua Bengio<\/a> (Montreal), <a href=\"http:\/\/nlp.csai.tsinghua.edu.cn\/~lzy\/\">Zhiyuan Liu<\/a> (Tsinghua), <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/taoqin\/\">Tao Qin<\/a> (MSR) and <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/tyliu\/\">Tie-Yan Liu<\/a> (MSR). <\/p>\n<div id=\"myplot_graph2019_last_author_source\" style=\"height:550px;\"><\/div>\n<p><script>\nvar trace_graph2019_last_author_source_0 = {  x: [\"Sergey Levine\", \"Graham Neubig\", \"Yoshua Bengio\", \"Zhiyuan Liu\", \"Tao Qin\", \"Tie-Yan Liu\", \"Xu Sun\", \"Ryan Cotterell\", \"William Yang Wang\", \"Shuming Shi\", \"Pieter Abbeel\", \"Yue Zhang\", \"Jianfeng Gao\", \"Caiming Xiong\", \"Yang Liu\", \"Ting Liu\", \"Dongyan Zhao\", \"Noah A. 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Smith<\/a> (Washington) and <a href=\"http:\/\/encs.hit.edu.cn\/2016\/0310\/c5511a145533\/page.htm\">Ting Liu<\/a> (Harbin) all have more than 90 papers from that period. I have had to remove Yang Liu from the list, as there seem to be two or more people publishing under this name and I was not able to automatically separate them.<\/p>\n<div id=\"myplot_graph2019_all_author_source\" style=\"height:550px;\"><\/div>\n<p><script>\nvar trace_graph2019_all_author_source_0 = {  x: [\"Yoshua Bengio\", \"Yang Liu\", \"Chris Dyer\", \"Ming Zhou\", \"Yue Zhang\", \"Noah A. Smith\", \"Ting Liu\", \"Lawrence Carin\", \"Graham Neubig\", \"Iryna Gurevych\", \"Sergey Levine\", \"Dan Roth\", \"Hinrich Sch\u00fctze\", \"Anders S\u00f8gaard\", \"Mirella Lapata\", \"Percy Liang\", \"Maosong Sun\", \"Christopher D. 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Ravikumar\", \"Yejin Choi\", \"Pushpak Bhattacharyya\", \"Tong Zhang\", \"Timothy Baldwin\", \"Min Zhang\", \"Jianfeng Gao\", \"Kyunghyun Cho\", \"Yoav Goldberg\", ],\n  y: [7, 28, 26, 39, 27, 27, 20, 8, 28, 15, 0, 13, 19, 19, 18, 17, 22, 23, 23, 25, 24, 4, 0, 23, 21, 20, 0, 29, 1, 15, 3, 16, 14, 24, 0, 20, 0, 19, 18, 2, 15, 15, 15, 8, 14, ],\n  name: 'ACL',\n  type: 'bar'\n};\nvar data_graph2019_all_author_source = [trace_graph2019_all_author_source_0, trace_graph2019_all_author_source_1, trace_graph2019_all_author_source_2, trace_graph2019_all_author_source_3, trace_graph2019_all_author_source_4, trace_graph2019_all_author_source_5, trace_graph2019_all_author_source_6, trace_graph2019_all_author_source_7, trace_graph2019_all_author_source_8, trace_graph2019_all_author_source_9, trace_graph2019_all_author_source_10, trace_graph2019_all_author_source_11];\nfixBarGraph(data_graph2019_all_author_source);\nscaleBarGraph(data_graph2019_all_author_source, 32);\nPlotly.newPlot('myplot_graph2019_all_author_source', data_graph2019_all_author_source, {barmode: 'stack', margin: { l: 30, r: 0, b: 170, t: 20, pad: 4 }});\n<\/script><\/p>\n<p>And looking at the separation in years shows that Sergey Levine, Graham Neubig and Yoshua Bengio have all overtaken the previous publication record set by Chris Dyer in 2016. They each also published considerably more than they did in the previous years. <\/p>\n<div id=\"myplot_graph2019_all_author_year\" style=\"height:550px;\"><\/div>\n<p><script>\nvar trace_graph2019_all_author_year_0 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [3, 9, 16, 8, 15, 17, 16, 29, ],\n  text: \"Yoshua Bengio\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_author_year_1 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [11, 9, 6, 18, 15, 14, 19, 17, ],\n  text: \"Yang Liu\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_author_year_2 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [3, 10, 14, 22, 25, 12, 11, 8, ],\n  text: \"Chris Dyer\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_author_year_3 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [13, 9, 14, 9, 12, 11, 21, 14, ],\n  text: \"Ming Zhou\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_author_year_4 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [3, 4, 13, 10, 23, 14, 13, 18, ],\n  text: \"Yue Zhang\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_author_year_5 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [9, 10, 14, 15, 10, 7, 13, 17, ],\n  text: \"Noah A. 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Manning\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_author_year_18 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [3, 9, 4, 6, 7, 6, 16, 16, ],\n  text: \"Luke Zettlemoyer\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_author_year_19 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [2, 6, 5, 12, 13, 11, 11, 7, ],\n  text: \"Trevor Cohn\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_author_year_20 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [4, 9, 7, 10, 4, 11, 9, 13, ],\n  text: \"Benjamin Van Durme\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_author_year_21 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [3, 4, 10, 4, 12, 5, 16, 12, ],\n  text: \"Jun Zhu\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_author_year_22 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [2, 0, 1, 4, 10, 13, 17, 19, ],\n  text: \"Pieter Abbeel\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_author_year_23 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [9, 8, 9, 4, 12, 11, 7, 5, ],\n  text: \"Qun Liu\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_author_year_24 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [4, 0, 2, 7, 5, 4, 18, 25, ],\n  text: \"Zhiyuan Liu\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_author_year_25 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [1, 5, 12, 5, 7, 9, 10, 16, ],\n  text: \"Eduard Hovy\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_author_year_26 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [2, 4, 5, 8, 10, 13, 9, 13, ],\n  text: \"Andreas Krause\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_author_year_27 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [2, 4, 10, 13, 9, 6, 10, 9, ],\n  text: \"Heng Ji\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_author_year_28 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [1, 5, 8, 5, 5, 11, 15, 12, ],\n  text: \"Le Song\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_author_year_29 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [13, 4, 7, 6, 7, 1, 12, 10, ],\n  text: \"Guodong Zhou\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_author_year_30 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [0, 2, 6, 1, 5, 10, 13, 23, ],\n  text: \"Tie-Yan Liu\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_author_year_31 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [3, 3, 3, 8, 13, 5, 12, 13, ],\n  text: \"Xuanjing Huang\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_author_year_32 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [2, 1, 3, 4, 10, 6, 18, 16, ],\n  text: \"Mohit Bansal\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_author_year_33 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [9, 8, 8, 8, 4, 10, 6, 6, ],\n  text: \"Dan Klein\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_author_year_34 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [1, 2, 2, 3, 11, 8, 15, 15, ],\n  text: \"Stefano Ermon\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_author_year_35 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [1, 1, 4, 1, 3, 4, 22, 21, ],\n  text: \"Xu Sun\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_author_year_36 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [2, 7, 14, 10, 5, 5, 10, 4, ],\n  text: \"Pradeep K. 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But now let's see the first authors, who are usually the people doing the actual implementation and writing. <a href=\"http:\/\/www.cs.cmu.edu\/~gfarina\/about\/\">Gabriele Farina<\/a> is a 4th year PhD at CMU and he has authored 6 papers in 2019, half of them at NeurIPS. <a href=\"http:\/\/www.iliasdiakonikolas.org\/\">Ilias Diakonikolas<\/a> (UW Madison), <a href=\"https:\/\/users.cs.duke.edu\/~hrzhang\/\">Hanrui Zhang<\/a> (Duke), <a href=\"https:\/\/www.ece.nus.edu.sg\/stfpage\/elezhang\/\">Rui Zhang<\/a> (NUS), <a href=\"https:\/\/wuch15.github.io\/\">Chuhan Wu<\/a> (Tsinghua), <a href=\"https:\/\/www.aclweb.org\/anthology\/people\/p\/pengcheng-yang\/\">Pengcheng Yang<\/a> (Peking), <a href=\"https:\/\/www.cs.princeton.edu\/~arora\/\">Sanjeev Arora<\/a> (Princeton), <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/zeyuana\/\">Zeyuan Allen-Zhu<\/a> (MSR) and <a href=\"https:\/\/moonfolk.github.io\/\">Mikhail Yurochkin<\/a> (IBM) were all first authors to 5 publications.<\/p>\n<div id=\"myplot_graph2019_last_firstauthor_source\" style=\"height:550px;\"><\/div>\n<p><script>\nvar trace_graph2019_last_firstauthor_source_0 = {  x: [\"Gabriele Farina\", \"Ilias Diakonikolas\", \"Hanrui Zhang\", \"Rui Zhang\", \"Chuhan Wu\", \"Pengcheng Yang\", \"Sanjeev Arora\", \"Zeyuan Allen-Zhu\", \"Mikhail Yurochkin\", \"Peng Xu\", \"Di Wang\", \"Yi Tay\", \"Hao Yu\", \"Shangtong Zhang\", \"Hao Zheng\", \"Minghao Hu\", \"Yi Hao\", \"Mikel Artetxe\", \"Xiao Zhang\", \"Hongming Zhang\", \"Elena Voita\", \"Kawin Ethayarajh\", \"Yue Wang\", \"Mingda Chen\", \"Dan Hendrycks\", \"Yu Bai\", \"Chulhee Yun\", \"Jayadev Acharya\", \"Nathan Kallus\", \"Zhao Song\", \"Zi-Yi Dou\", \"Tao Gui\", \"Baosong Yang\", \"Yang Zhao\", \"Sawan Kumar\", \"Hao Wang\", \"Hao Liu\", \"Xiao Liu\", \"Patrick Schwab\", \"Bryan Wilder\", \"Maarten Sap\", \"Lizhong Ding\", \"Amirata Ghorbani\", \"Ping Li\", \"Zheng Li\", ],\n  y: [1, 1, 3, 0, 0, 0, 0, 0, 0, 1, 2, 1, 1, 2, 2, 1, 1, 0, 0, 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href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/zeyuana\/\">Zeyuan Allen-Zhu<\/a> (MSR), <a href=\"https:\/\/nlp.stanford.edu\/~bdlijiwei\/\">Jiwei Li<\/a> (Shannon AI), <a href=\"https:\/\/sites.google.com\/site\/ivanvulic\/\">Ivan Vuli\u0107<\/a> (Cambridge), <a href=\"https:\/\/ryancotterell.github.io\/\">Ryan Cotterell<\/a> (Cambridge), <a href=\"http:\/\/pages.cs.wisc.edu\/~ybkim\/\">Young-Bum Kim<\/a> (Amazon) and <a href=\"https:\/\/www.cs.princeton.edu\/~arora\/\">Sanjeev Arora<\/a> (Princeton) have published the most overall.<\/p>\n<div id=\"myplot_graph2019_all_firstauthor_source\" style=\"height:550px;\"><\/div>\n<p><script>\nvar trace_graph2019_all_firstauthor_source_0 = {  x: [\"Yang Liu\", \"Zeyuan Allen-Zhu\", \"Jiwei Li\", \"Ivan Vuli\u0107\", \"Ryan Cotterell\", \"Young-Bum Kim\", \"Sanjeev Arora\", \"Wenpeng Yin\", \"Elad Hazan\", \"Yi Tay\", \"Chen Chen\", \"Meishan Zhang\", \"Corinna Cortes\", \"Omer Levy\", \"Lijun Zhang\", \"Haris Aziz\", \"Yining 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Zhang\", \"Corinna Cortes\", \"Omer Levy\", \"Lijun Zhang\", \"Haris Aziz\", \"Yining Wang\", \"Yuan Zhang\", \"Shafiq Joty\", \"Jacob Andreas\", \"Ellie Pavlick\", \"Min Xiao\", \"Yang Yang\", \"Qiang Liu\", \"Di Wang\", \"Jiatao Gu\", \"Douwe Kiela\", \"Yi Yang\", \"Yuchen Zhang\", \"Li Dong\", \"Hao Wang\", \"Anders S\u00f8gaard\", \"Gabriele Farina\", \"Mikel Artetxe\", \"Angeliki Lazaridou\", \"Xuezhe Ma\", \"Manaal Faruqui\", \"Annie Louis\", \"Dirk Hovy\", \"Zhilin Yang\", \"Jayadev Acharya\", \"Feiping Nie\", \"Pengtao Xie\", \"Ziqiang Cao\", \"Hao Li\", ],\n  y: [1, 0, 2, 3, 6, 3, 0, 2, 0, 0, 1, 2, 0, 1, 0, 0, 0, 3, 1, 3, 1, 0, 0, 0, 0, 1, 1, 4, 0, 0, 0, 2, 0, 0, 2, 1, 3, 1, 2, 0, 0, 0, 1, 0, 2, ],\n  name: 'NAACL',\n  type: 'bar'\n};\nvar trace_graph2019_all_firstauthor_source_10 = {  x: [\"Yang Liu\", \"Zeyuan Allen-Zhu\", \"Jiwei Li\", \"Ivan Vuli\u0107\", \"Ryan Cotterell\", \"Young-Bum Kim\", \"Sanjeev Arora\", \"Wenpeng Yin\", \"Elad Hazan\", \"Yi Tay\", \"Chen Chen\", \"Meishan Zhang\", \"Corinna Cortes\", \"Omer Levy\", \"Lijun Zhang\", \"Haris Aziz\", \"Yining Wang\", \"Yuan Zhang\", \"Shafiq Joty\", \"Jacob Andreas\", \"Ellie Pavlick\", \"Min Xiao\", \"Yang Yang\", \"Qiang Liu\", \"Di Wang\", \"Jiatao Gu\", \"Douwe Kiela\", \"Yi Yang\", \"Yuchen Zhang\", \"Li Dong\", \"Hao Wang\", \"Anders S\u00f8gaard\", \"Gabriele Farina\", \"Mikel Artetxe\", \"Angeliki Lazaridou\", \"Xuezhe Ma\", \"Manaal Faruqui\", \"Annie Louis\", \"Dirk Hovy\", \"Zhilin Yang\", \"Jayadev Acharya\", \"Feiping Nie\", \"Pengtao Xie\", \"Ziqiang Cao\", \"Hao Li\", ],\n  y: [5, 0, 10, 4, 3, 3, 0, 3, 0, 4, 2, 4, 0, 0, 0, 0, 2, 4, 5, 3, 2, 2, 2, 0, 2, 2, 7, 5, 3, 1, 3, 1, 0, 2, 1, 2, 2, 2, 2, 1, 0, 0, 0, 1, 1, ],\n  name: 'EMNLP',\n  type: 'bar'\n};\nvar trace_graph2019_all_firstauthor_source_11 = {  x: [\"Yang Liu\", \"Zeyuan Allen-Zhu\", \"Jiwei Li\", \"Ivan Vuli\u0107\", \"Ryan Cotterell\", \"Young-Bum Kim\", \"Sanjeev Arora\", \"Wenpeng Yin\", \"Elad Hazan\", \"Yi Tay\", \"Chen Chen\", \"Meishan Zhang\", \"Corinna Cortes\", \"Omer Levy\", \"Lijun Zhang\", \"Haris Aziz\", \"Yining Wang\", \"Yuan Zhang\", \"Shafiq Joty\", \"Jacob Andreas\", \"Ellie Pavlick\", \"Min Xiao\", \"Yang Yang\", \"Qiang Liu\", \"Di Wang\", \"Jiatao Gu\", \"Douwe Kiela\", \"Yi Yang\", \"Yuchen Zhang\", \"Li Dong\", \"Hao Wang\", \"Anders S\u00f8gaard\", \"Gabriele Farina\", \"Mikel Artetxe\", \"Angeliki Lazaridou\", \"Xuezhe Ma\", \"Manaal Faruqui\", \"Annie Louis\", \"Dirk Hovy\", \"Zhilin Yang\", \"Jayadev Acharya\", \"Feiping Nie\", \"Pengtao Xie\", \"Ziqiang Cao\", \"Hao Li\", ],\n  y: [3, 0, 6, 6, 3, 9, 0, 3, 0, 3, 2, 3, 1, 4, 0, 0, 1, 4, 3, 3, 7, 1, 0, 0, 1, 3, 3, 2, 0, 5, 0, 4, 0, 5, 5, 4, 3, 1, 6, 1, 0, 0, 2, 2, 2, ],\n  name: 'ACL',\n  type: 'bar'\n};\nvar data_graph2019_all_firstauthor_source = [trace_graph2019_all_firstauthor_source_0, trace_graph2019_all_firstauthor_source_1, trace_graph2019_all_firstauthor_source_2, trace_graph2019_all_firstauthor_source_3, trace_graph2019_all_firstauthor_source_4, trace_graph2019_all_firstauthor_source_5, trace_graph2019_all_firstauthor_source_6, trace_graph2019_all_firstauthor_source_7, trace_graph2019_all_firstauthor_source_8, trace_graph2019_all_firstauthor_source_9, trace_graph2019_all_firstauthor_source_10, trace_graph2019_all_firstauthor_source_11];\nfixBarGraph(data_graph2019_all_firstauthor_source);\nscaleBarGraph(data_graph2019_all_firstauthor_source, 28);\nPlotly.newPlot('myplot_graph2019_all_firstauthor_source', data_graph2019_all_firstauthor_source, {barmode: 'stack', margin: { l: 30, r: 0, b: 170, t: 20, pad: 4 }});\n<\/script><\/p>\n<h2>Countries<\/h2>\n<p>For the first time we are now actually able to analyse which countries published the most in 2019. Admittedly, this graph mainly highlights just how much the US dominates the research in this area. China, UK, Germany and Canada are also putting in a strong effort. China has a proportionally very large presence at AAAI, whereas the US is publishing more in NeurIPS and ICML.<\/p>\n<div id=\"myplot_graph2019_last_country_source\" style=\"height:550px;\"><\/div>\n<p><script>\nvar trace_graph2019_last_country_source_0 = {  x: [\"USA\", \"China\", \"UK\", \"Germany\", \"Canada\", \"Japan\", \"France\", \"Australia\", \"Singapore\", \"Switzerland\", \"Israel\", \"India\", \"South Korea\", \"Netherlands\", \"Italy\", \"Denmark\", \"Spain\", \"Taiwan\", \"Russia\", \"Finland\", \"Poland\", \"Sweden\", \"Macau\", \"Austria\", \"Qatar\", \"Czech Republic\", \"Hong Kong\", \"Belgium\", \"Iran\", \"Saudi Arabia\", \"Greece\", \"Brazil\", \"Ireland\", \"Norway\", \"Portugal\", \"Romania\", \"New Zealand\", \"Turkey\", \"UAE\", \"Luxembourg\", \"South Africa\", \"Bulgaria\", \"Chile\", \"Hungary\", \"Thailand\", ],\n  y: [488, 489, 74, 44, 55, 41, 15, 58, 53, 16, 22, 26, 20, 13, 13, 4, 7, 11, 0, 6, 10, 3, 5, 6, 5, 7, 6, 1, 4, 7, 2, 5, 2, 4, 4, 0, 2, 1, 0, 1, 0, 0, 0, 1, 1, ],\n  name: 'AAAI',\n  type: 'bar'\n};\nvar trace_graph2019_last_country_source_1 = {  x: [\"USA\", \"China\", \"UK\", \"Germany\", \"Canada\", \"Japan\", \"France\", \"Australia\", \"Singapore\", \"Switzerland\", \"Israel\", \"India\", \"South Korea\", \"Netherlands\", \"Italy\", \"Denmark\", \"Spain\", \"Taiwan\", \"Russia\", \"Finland\", \"Poland\", \"Sweden\", \"Macau\", \"Austria\", \"Qatar\", \"Czech Republic\", \"Hong Kong\", \"Belgium\", \"Iran\", \"Saudi Arabia\", \"Greece\", \"Brazil\", \"Ireland\", \"Norway\", \"Portugal\", \"Romania\", \"New Zealand\", \"Turkey\", \"UAE\", \"Luxembourg\", \"South Africa\", \"Bulgaria\", \"Chile\", \"Hungary\", \"Thailand\", ],\n  y: [321, 59, 74, 27, 43, 7, 9, 8, 3, 13, 7, 2, 10, 8, 3, 1, 0, 2, 4, 1, 2, 0, 1, 1, 1, 2, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, ],\n  name: 'ICLR',\n  type: 'bar'\n};\nvar trace_graph2019_last_country_source_2 = {  x: [\"USA\", \"China\", \"UK\", \"Germany\", \"Canada\", \"Japan\", \"France\", \"Australia\", \"Singapore\", \"Switzerland\", \"Israel\", \"India\", \"South Korea\", \"Netherlands\", \"Italy\", \"Denmark\", \"Spain\", \"Taiwan\", \"Russia\", \"Finland\", \"Poland\", \"Sweden\", \"Macau\", \"Austria\", \"Qatar\", \"Czech Republic\", \"Hong Kong\", \"Belgium\", \"Iran\", \"Saudi Arabia\", \"Greece\", \"Brazil\", \"Ireland\", \"Norway\", \"Portugal\", \"Romania\", \"New Zealand\", \"Turkey\", \"UAE\", \"Luxembourg\", \"South Africa\", \"Bulgaria\", \"Chile\", \"Hungary\", \"Thailand\", ],\n  y: [428, 55, 84, 37, 36, 17, 33, 13, 5, 26, 19, 6, 12, 5, 7, 4, 2, 2, 6, 3, 5, 2, 0, 4, 0, 2, 1, 3, 1, 2, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, ],\n  name: 'ICML',\n  type: 'bar'\n};\nvar trace_graph2019_last_country_source_3 = {  x: [\"USA\", \"China\", \"UK\", \"Germany\", \"Canada\", \"Japan\", \"France\", \"Australia\", \"Singapore\", \"Switzerland\", \"Israel\", \"India\", \"South Korea\", \"Netherlands\", \"Italy\", \"Denmark\", \"Spain\", \"Taiwan\", \"Russia\", \"Finland\", \"Poland\", \"Sweden\", \"Macau\", \"Austria\", \"Qatar\", \"Czech Republic\", \"Hong Kong\", \"Belgium\", \"Iran\", \"Saudi Arabia\", \"Greece\", \"Brazil\", \"Ireland\", \"Norway\", \"Portugal\", \"Romania\", \"New Zealand\", \"Turkey\", \"UAE\", \"Luxembourg\", \"South Africa\", \"Bulgaria\", \"Chile\", \"Hungary\", \"Thailand\", ],\n  y: [876, 181, 152, 58, 76, 31, 77, 33, 22, 51, 34, 15, 22, 15, 18, 9, 7, 5, 9, 11, 2, 9, 2, 5, 0, 0, 3, 4, 3, 2, 1, 1, 0, 1, 0, 2, 1, 1, 1, 0, 0, 0, 1, 0, 0, ],\n  name: 'NeurIPS',\n  type: 'bar'\n};\nvar trace_graph2019_last_country_source_4 = {  x: [\"USA\", \"China\", \"UK\", \"Germany\", \"Canada\", \"Japan\", \"France\", \"Australia\", \"Singapore\", \"Switzerland\", \"Israel\", \"India\", \"South Korea\", \"Netherlands\", \"Italy\", \"Denmark\", \"Spain\", \"Taiwan\", \"Russia\", \"Finland\", \"Poland\", \"Sweden\", \"Macau\", \"Austria\", \"Qatar\", \"Czech Republic\", \"Hong Kong\", \"Belgium\", \"Iran\", \"Saudi Arabia\", \"Greece\", \"Brazil\", \"Ireland\", \"Norway\", \"Portugal\", \"Romania\", \"New Zealand\", \"Turkey\", \"UAE\", \"Luxembourg\", \"South Africa\", \"Bulgaria\", \"Chile\", \"Hungary\", \"Thailand\", ],\n  y: [7, 2, 5, 2, 1, 3, 1, 0, 1, 0, 0, 0, 0, 0, 2, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, ],\n  name: 'CL',\n  type: 'bar'\n};\nvar trace_graph2019_last_country_source_5 = {  x: [\"USA\", \"China\", \"UK\", \"Germany\", \"Canada\", \"Japan\", \"France\", \"Australia\", \"Singapore\", \"Switzerland\", \"Israel\", \"India\", \"South Korea\", \"Netherlands\", \"Italy\", \"Denmark\", \"Spain\", \"Taiwan\", \"Russia\", \"Finland\", \"Poland\", \"Sweden\", \"Macau\", \"Austria\", \"Qatar\", \"Czech Republic\", \"Hong Kong\", \"Belgium\", \"Iran\", \"Saudi Arabia\", \"Greece\", \"Brazil\", \"Ireland\", \"Norway\", \"Portugal\", \"Romania\", \"New Zealand\", \"Turkey\", \"UAE\", \"Luxembourg\", \"South Africa\", \"Bulgaria\", \"Chile\", \"Hungary\", \"Thailand\", ],\n  y: [26, 5, 8, 4, 0, 0, 1, 0, 1, 2, 2, 5, 1, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, ],\n  name: 'TACL',\n  type: 'bar'\n};\nvar trace_graph2019_last_country_source_6 = {  x: [\"USA\", \"China\", \"UK\", \"Germany\", \"Canada\", \"Japan\", \"France\", \"Australia\", \"Singapore\", \"Switzerland\", \"Israel\", \"India\", \"South Korea\", \"Netherlands\", \"Italy\", \"Denmark\", \"Spain\", \"Taiwan\", \"Russia\", \"Finland\", \"Poland\", \"Sweden\", \"Macau\", \"Austria\", \"Qatar\", \"Czech Republic\", \"Hong Kong\", \"Belgium\", \"Iran\", \"Saudi Arabia\", \"Greece\", \"Brazil\", \"Ireland\", \"Norway\", \"Portugal\", \"Romania\", \"New Zealand\", \"Turkey\", \"UAE\", \"Luxembourg\", \"South Africa\", \"Bulgaria\", \"Chile\", \"Hungary\", \"Thailand\", ],\n  y: [45, 16, 7, 9, 5, 5, 2, 2, 1, 3, 6, 3, 2, 2, 0, 2, 0, 0, 0, 2, 2, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, ],\n  name: 'CoNLL',\n  type: 'bar'\n};\nvar trace_graph2019_last_country_source_7 = {  x: [\"USA\", \"China\", \"UK\", \"Germany\", \"Canada\", \"Japan\", \"France\", \"Australia\", \"Singapore\", \"Switzerland\", \"Israel\", \"India\", \"South Korea\", \"Netherlands\", \"Italy\", \"Denmark\", \"Spain\", \"Taiwan\", \"Russia\", \"Finland\", \"Poland\", \"Sweden\", \"Macau\", \"Austria\", \"Qatar\", \"Czech Republic\", \"Hong Kong\", \"Belgium\", \"Iran\", \"Saudi Arabia\", \"Greece\", \"Brazil\", \"Ireland\", \"Norway\", \"Portugal\", \"Romania\", \"New Zealand\", \"Turkey\", \"UAE\", \"Luxembourg\", \"South Africa\", \"Bulgaria\", \"Chile\", \"Hungary\", \"Thailand\", ],\n  y: [263, 64, 50, 32, 19, 23, 8, 12, 11, 6, 14, 11, 7, 8, 5, 9, 6, 2, 0, 0, 0, 2, 4, 0, 4, 2, 0, 4, 5, 0, 4, 0, 1, 0, 2, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, ],\n  name: 'NAACL',\n  type: 'bar'\n};\nvar trace_graph2019_last_country_source_8 = {  x: [\"USA\", \"China\", \"UK\", \"Germany\", \"Canada\", \"Japan\", \"France\", \"Australia\", \"Singapore\", \"Switzerland\", \"Israel\", \"India\", \"South Korea\", \"Netherlands\", \"Italy\", \"Denmark\", \"Spain\", \"Taiwan\", \"Russia\", \"Finland\", \"Poland\", \"Sweden\", \"Macau\", \"Austria\", \"Qatar\", \"Czech Republic\", \"Hong Kong\", \"Belgium\", \"Iran\", \"Saudi Arabia\", \"Greece\", \"Brazil\", \"Ireland\", \"Norway\", \"Portugal\", \"Romania\", \"New Zealand\", \"Turkey\", \"UAE\", \"Luxembourg\", \"South Africa\", \"Bulgaria\", \"Chile\", \"Hungary\", \"Thailand\", ],\n  y: [346, 209, 58, 31, 25, 20, 7, 6, 22, 8, 13, 11, 12, 10, 7, 6, 2, 8, 4, 3, 0, 4, 0, 0, 4, 1, 2, 0, 0, 2, 1, 1, 3, 0, 0, 2, 1, 0, 1, 0, 0, 2, 1, 0, 1, ],\n  name: 'EMNLP',\n  type: 'bar'\n};\nvar trace_graph2019_last_country_source_9 = {  x: [\"USA\", \"China\", \"UK\", \"Germany\", \"Canada\", \"Japan\", \"France\", \"Australia\", \"Singapore\", \"Switzerland\", \"Israel\", \"India\", \"South Korea\", \"Netherlands\", \"Italy\", \"Denmark\", \"Spain\", \"Taiwan\", \"Russia\", \"Finland\", \"Poland\", \"Sweden\", \"Macau\", \"Austria\", \"Qatar\", \"Czech Republic\", \"Hong Kong\", \"Belgium\", \"Iran\", \"Saudi Arabia\", \"Greece\", \"Brazil\", \"Ireland\", \"Norway\", \"Portugal\", \"Romania\", \"New Zealand\", \"Turkey\", \"UAE\", \"Luxembourg\", \"South Africa\", \"Bulgaria\", \"Chile\", \"Hungary\", \"Thailand\", ],\n  y: [308, 168, 57, 50, 19, 25, 5, 12, 25, 10, 8, 22, 8, 15, 7, 6, 12, 4, 7, 1, 1, 1, 6, 1, 0, 2, 0, 1, 0, 0, 3, 1, 2, 1, 1, 2, 1, 0, 2, 1, 1, 0, 0, 1, 0, ],\n  name: 'ACL',\n  type: 'bar'\n};\nvar data_graph2019_last_country_source = [trace_graph2019_last_country_source_0, trace_graph2019_last_country_source_1, trace_graph2019_last_country_source_2, trace_graph2019_last_country_source_3, trace_graph2019_last_country_source_4, trace_graph2019_last_country_source_5, trace_graph2019_last_country_source_6, trace_graph2019_last_country_source_7, trace_graph2019_last_country_source_8, trace_graph2019_last_country_source_9];\nfixBarGraph(data_graph2019_last_country_source);\nscaleBarGraph(data_graph2019_last_country_source, 32);\nPlotly.newPlot('myplot_graph2019_last_country_source', data_graph2019_last_country_source, {barmode: 'stack', margin: { l: 30, r: 0, b: 100, t: 20, pad: 4 }});\n<\/script><\/p>\n<p>The picture looks very similar when looking at the whole 2012-2019 period.<\/p>\n<div id=\"myplot_graph2019_all_country_source\" style=\"height:550px;\"><\/div>\n<p><script>\nvar trace_graph2019_all_country_source_0 = {  x: [\"USA\", \"China\", \"UK\", \"Germany\", \"Canada\", \"Japan\", \"France\", \"Australia\", \"Singapore\", \"Israel\", \"Switzerland\", \"Italy\", \"India\", \"Netherlands\", \"South Korea\", \"Spain\", \"Denmark\", \"Sweden\", \"Ireland\", \"Taiwan\", \"Qatar\", \"Finland\", \"Austria\", \"Russia\", \"Belgium\", \"Poland\", \"Czech Republic\", \"Greece\", \"Portugal\", \"Iran\", \"Brazil\", \"Norway\", \"Hong Kong\", \"Turkey\", \"Saudi Arabia\", \"Macau\", \"Romania\", \"UAE\", \"New Zealand\", \"Chile\", \"Hungary\", \"South Africa\", \"Croatia\", \"Egypt\", \"Vietnam\", ],\n  y: [2126, 1548, 355, 184, 227, 163, 115, 279, 236, 128, 90, 103, 91, 49, 59, 34, 19, 26, 12, 28, 6, 22, 41, 3, 17, 34, 19, 27, 20, 10, 14, 8, 22, 4, 18, 7, 2, 5, 11, 7, 2, 3, 1, 2, 2, ],\n  name: 'AAAI',\n  type: 'bar'\n};\nvar trace_graph2019_all_country_source_1 = {  x: [\"USA\", \"China\", \"UK\", \"Germany\", \"Canada\", \"Japan\", \"France\", \"Australia\", \"Singapore\", \"Israel\", \"Switzerland\", \"Italy\", \"India\", \"Netherlands\", \"South Korea\", \"Spain\", \"Denmark\", \"Sweden\", \"Ireland\", \"Taiwan\", \"Qatar\", \"Finland\", \"Austria\", \"Russia\", \"Belgium\", \"Poland\", \"Czech Republic\", \"Greece\", \"Portugal\", \"Iran\", \"Brazil\", \"Norway\", \"Hong Kong\", \"Turkey\", \"Saudi Arabia\", \"Macau\", \"Romania\", \"UAE\", \"New Zealand\", \"Chile\", \"Hungary\", \"South Africa\", \"Croatia\", \"Egypt\", \"Vietnam\", ],\n  y: [787, 95, 175, 57, 143, 20, 34, 12, 9, 20, 25, 10, 10, 28, 19, 6, 3, 0, 1, 3, 1, 6, 7, 7, 4, 5, 3, 0, 2, 0, 1, 0, 2, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, ],\n  name: 'ICLR',\n  type: 'bar'\n};\nvar trace_graph2019_all_country_source_2 = {  x: [\"USA\", \"China\", \"UK\", \"Germany\", \"Canada\", \"Japan\", \"France\", \"Australia\", \"Singapore\", \"Israel\", \"Switzerland\", \"Italy\", \"India\", \"Netherlands\", \"South Korea\", \"Spain\", \"Denmark\", \"Sweden\", \"Ireland\", \"Taiwan\", \"Qatar\", \"Finland\", \"Austria\", \"Russia\", \"Belgium\", \"Poland\", \"Czech Republic\", \"Greece\", \"Portugal\", \"Iran\", \"Brazil\", \"Norway\", \"Hong Kong\", \"Turkey\", \"Saudi Arabia\", \"Macau\", \"Romania\", \"UAE\", \"New Zealand\", \"Chile\", \"Hungary\", \"South Africa\", \"Croatia\", \"Egypt\", \"Vietnam\", ],\n  y: [1864, 219, 356, 150, 200, 87, 158, 73, 54, 110, 131, 32, 41, 28, 32, 16, 11, 12, 1, 9, 1, 18, 13, 21, 10, 11, 4, 8, 3, 5, 4, 2, 2, 3, 4, 1, 0, 0, 4, 2, 0, 2, 0, 0, 0, ],\n  name: 'ICML',\n  type: 'bar'\n};\nvar trace_graph2019_all_country_source_3 = {  x: [\"USA\", \"China\", \"UK\", \"Germany\", \"Canada\", \"Japan\", \"France\", \"Australia\", \"Singapore\", \"Israel\", \"Switzerland\", \"Italy\", \"India\", \"Netherlands\", \"South Korea\", \"Spain\", \"Denmark\", \"Sweden\", \"Ireland\", \"Taiwan\", \"Qatar\", \"Finland\", \"Austria\", \"Russia\", \"Belgium\", \"Poland\", \"Czech Republic\", \"Greece\", \"Portugal\", \"Iran\", \"Brazil\", \"Norway\", \"Hong Kong\", \"Turkey\", \"Saudi Arabia\", \"Macau\", \"Romania\", \"UAE\", \"New Zealand\", \"Chile\", \"Hungary\", \"South Africa\", \"Croatia\", \"Egypt\", \"Vietnam\", ],\n  y: [3290, 483, 534, 204, 292, 116, 277, 100, 76, 134, 193, 80, 51, 41, 62, 34, 30, 27, 0, 12, 1, 33, 24, 31, 20, 10, 3, 9, 4, 6, 5, 4, 10, 4, 5, 3, 3, 2, 1, 5, 0, 2, 0, 0, 0, ],\n  name: 'NeurIPS',\n  type: 'bar'\n};\nvar trace_graph2019_all_country_source_4 = {  x: [\"USA\", \"China\", \"UK\", \"Germany\", \"Canada\", \"Japan\", \"France\", \"Australia\", \"Singapore\", \"Israel\", \"Switzerland\", \"Italy\", \"India\", \"Netherlands\", \"South Korea\", \"Spain\", \"Denmark\", \"Sweden\", \"Ireland\", \"Taiwan\", \"Qatar\", \"Finland\", \"Austria\", \"Russia\", \"Belgium\", \"Poland\", \"Czech Republic\", \"Greece\", \"Portugal\", \"Iran\", \"Brazil\", \"Norway\", \"Hong Kong\", \"Turkey\", \"Saudi Arabia\", \"Macau\", \"Romania\", \"UAE\", \"New Zealand\", \"Chile\", \"Hungary\", \"South Africa\", \"Croatia\", \"Egypt\", \"Vietnam\", ],\n  y: [106, 17, 51, 34, 12, 5, 16, 3, 8, 12, 6, 21, 0, 15, 0, 16, 3, 18, 2, 0, 4, 1, 0, 1, 4, 0, 1, 0, 2, 1, 1, 2, 0, 2, 0, 0, 2, 1, 0, 0, 1, 0, 0, 0, 0, ],\n  name: 'CL',\n  type: 'bar'\n};\nvar trace_graph2019_all_country_source_5 = {  x: [\"USA\", \"China\", \"UK\", \"Germany\", \"Canada\", \"Japan\", \"France\", \"Australia\", \"Singapore\", \"Israel\", \"Switzerland\", \"Italy\", \"India\", \"Netherlands\", \"South Korea\", \"Spain\", \"Denmark\", \"Sweden\", \"Ireland\", \"Taiwan\", \"Qatar\", \"Finland\", \"Austria\", \"Russia\", \"Belgium\", \"Poland\", \"Czech Republic\", \"Greece\", \"Portugal\", \"Iran\", \"Brazil\", \"Norway\", \"Hong Kong\", \"Turkey\", \"Saudi Arabia\", \"Macau\", \"Romania\", \"UAE\", \"New Zealand\", \"Chile\", \"Hungary\", \"South Africa\", \"Croatia\", \"Egypt\", \"Vietnam\", ],\n  y: [171, 20, 42, 24, 9, 5, 3, 12, 5, 14, 4, 9, 9, 5, 1, 5, 1, 8, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 2, 0, 1, 0, 0, ],\n  name: 'TACL',\n  type: 'bar'\n};\nvar trace_graph2019_all_country_source_6 = {  x: [\"USA\", \"China\", \"UK\", \"Germany\", \"Canada\", \"Japan\", \"France\", \"Australia\", \"Singapore\", \"Israel\", \"Switzerland\", \"Italy\", \"India\", \"Netherlands\", \"South Korea\", \"Spain\", \"Denmark\", \"Sweden\", \"Ireland\", \"Taiwan\", \"Qatar\", \"Finland\", \"Austria\", \"Russia\", \"Belgium\", \"Poland\", \"Czech Republic\", \"Greece\", \"Portugal\", \"Iran\", \"Brazil\", \"Norway\", \"Hong Kong\", \"Turkey\", \"Saudi Arabia\", \"Macau\", \"Romania\", \"UAE\", \"New Zealand\", \"Chile\", \"Hungary\", \"South Africa\", \"Croatia\", \"Egypt\", \"Vietnam\", ],\n  y: [138, 27, 31, 31, 10, 17, 6, 16, 5, 19, 11, 6, 14, 8, 4, 6, 9, 2, 3, 0, 8, 4, 1, 1, 2, 2, 1, 0, 1, 0, 0, 3, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 2, ],\n  name: 'CoNLL',\n  type: 'bar'\n};\nvar trace_graph2019_all_country_source_7 = {  x: [\"USA\", \"China\", \"UK\", \"Germany\", \"Canada\", \"Japan\", \"France\", \"Australia\", \"Singapore\", \"Israel\", \"Switzerland\", \"Italy\", \"India\", \"Netherlands\", \"South Korea\", \"Spain\", \"Denmark\", \"Sweden\", \"Ireland\", \"Taiwan\", \"Qatar\", \"Finland\", \"Austria\", \"Russia\", \"Belgium\", \"Poland\", \"Czech Republic\", \"Greece\", \"Portugal\", \"Iran\", \"Brazil\", \"Norway\", \"Hong Kong\", \"Turkey\", \"Saudi Arabia\", \"Macau\", \"Romania\", \"UAE\", \"New Zealand\", \"Chile\", \"Hungary\", \"South Africa\", \"Croatia\", \"Egypt\", \"Vietnam\", ],\n  y: [327, 273, 87, 129, 42, 88, 53, 29, 39, 12, 9, 26, 50, 38, 12, 29, 8, 16, 29, 15, 12, 2, 6, 5, 9, 6, 9, 3, 2, 6, 2, 5, 0, 7, 2, 1, 2, 6, 2, 1, 3, 2, 4, 4, 3, ],\n  name: 'COLING',\n  type: 'bar'\n};\nvar trace_graph2019_all_country_source_8 = {  x: [\"USA\", \"China\", \"UK\", \"Germany\", \"Canada\", \"Japan\", \"France\", \"Australia\", \"Singapore\", \"Israel\", \"Switzerland\", \"Italy\", \"India\", \"Netherlands\", \"South Korea\", \"Spain\", \"Denmark\", \"Sweden\", \"Ireland\", \"Taiwan\", \"Qatar\", \"Finland\", \"Austria\", \"Russia\", \"Belgium\", \"Poland\", \"Czech Republic\", \"Greece\", \"Portugal\", \"Iran\", \"Brazil\", \"Norway\", \"Hong Kong\", \"Turkey\", \"Saudi Arabia\", \"Macau\", \"Romania\", \"UAE\", \"New Zealand\", \"Chile\", \"Hungary\", \"South Africa\", \"Croatia\", \"Egypt\", \"Vietnam\", ],\n  y: [146, 13, 65, 84, 10, 19, 22, 20, 5, 8, 8, 21, 11, 20, 0, 14, 9, 7, 7, 1, 5, 4, 2, 1, 2, 2, 5, 4, 2, 1, 1, 1, 0, 1, 0, 0, 5, 1, 0, 0, 3, 0, 0, 0, 1, ],\n  name: 'EACL',\n  type: 'bar'\n};\nvar trace_graph2019_all_country_source_9 = {  x: [\"USA\", \"China\", \"UK\", \"Germany\", \"Canada\", \"Japan\", \"France\", \"Australia\", \"Singapore\", \"Israel\", \"Switzerland\", \"Italy\", \"India\", \"Netherlands\", \"South Korea\", \"Spain\", \"Denmark\", \"Sweden\", \"Ireland\", \"Taiwan\", \"Qatar\", \"Finland\", \"Austria\", \"Russia\", \"Belgium\", \"Poland\", \"Czech Republic\", \"Greece\", \"Portugal\", \"Iran\", \"Brazil\", \"Norway\", \"Hong Kong\", \"Turkey\", \"Saudi Arabia\", \"Macau\", \"Romania\", \"UAE\", \"New Zealand\", \"Chile\", \"Hungary\", \"South Africa\", \"Croatia\", \"Egypt\", \"Vietnam\", ],\n  y: [823, 124, 153, 114, 62, 60, 27, 37, 26, 32, 12, 22, 36, 19, 18, 21, 23, 9, 9, 10, 16, 1, 0, 0, 6, 1, 4, 4, 2, 8, 1, 3, 0, 3, 0, 4, 1, 2, 0, 0, 0, 3, 1, 1, 1, ],\n  name: 'NAACL',\n  type: 'bar'\n};\nvar trace_graph2019_all_country_source_10 = {  x: [\"USA\", \"China\", \"UK\", \"Germany\", \"Canada\", \"Japan\", \"France\", \"Australia\", \"Singapore\", \"Israel\", \"Switzerland\", \"Italy\", \"India\", \"Netherlands\", \"South Korea\", \"Spain\", \"Denmark\", \"Sweden\", \"Ireland\", \"Taiwan\", \"Qatar\", \"Finland\", \"Austria\", \"Russia\", \"Belgium\", \"Poland\", \"Czech Republic\", \"Greece\", \"Portugal\", \"Iran\", \"Brazil\", \"Norway\", \"Hong Kong\", \"Turkey\", \"Saudi Arabia\", \"Macau\", \"Romania\", \"UAE\", \"New Zealand\", \"Chile\", \"Hungary\", \"South Africa\", \"Croatia\", \"Egypt\", \"Vietnam\", ],\n  y: [1352, 596, 234, 171, 83, 110, 58, 47, 105, 40, 24, 46, 58, 41, 27, 25, 25, 20, 25, 22, 22, 3, 1, 7, 10, 3, 6, 3, 3, 0, 2, 5, 2, 7, 2, 4, 6, 6, 1, 2, 2, 0, 0, 2, 1, ],\n  name: 'EMNLP',\n  type: 'bar'\n};\nvar trace_graph2019_all_country_source_11 = {  x: [\"USA\", \"China\", \"UK\", \"Germany\", \"Canada\", \"Japan\", \"France\", \"Australia\", \"Singapore\", \"Israel\", \"Switzerland\", \"Italy\", \"India\", \"Netherlands\", \"South Korea\", \"Spain\", \"Denmark\", \"Sweden\", \"Ireland\", \"Taiwan\", \"Qatar\", \"Finland\", \"Austria\", \"Russia\", \"Belgium\", \"Poland\", \"Czech Republic\", \"Greece\", \"Portugal\", \"Iran\", \"Brazil\", \"Norway\", \"Hong Kong\", \"Turkey\", \"Saudi Arabia\", \"Macau\", \"Romania\", \"UAE\", \"New Zealand\", \"Chile\", \"Hungary\", \"South Africa\", \"Croatia\", \"Egypt\", \"Vietnam\", ],\n  y: [1345, 567, 251, 218, 83, 142, 54, 75, 96, 64, 23, 67, 63, 51, 29, 35, 34, 13, 28, 18, 33, 6, 1, 18, 4, 5, 12, 6, 11, 5, 9, 6, 0, 2, 1, 9, 8, 3, 2, 0, 4, 1, 5, 3, 2, ],\n  name: 'ACL',\n  type: 'bar'\n};\nvar data_graph2019_all_country_source = [trace_graph2019_all_country_source_0, trace_graph2019_all_country_source_1, trace_graph2019_all_country_source_2, trace_graph2019_all_country_source_3, trace_graph2019_all_country_source_4, trace_graph2019_all_country_source_5, trace_graph2019_all_country_source_6, trace_graph2019_all_country_source_7, trace_graph2019_all_country_source_8, trace_graph2019_all_country_source_9, trace_graph2019_all_country_source_10, trace_graph2019_all_country_source_11];\nfixBarGraph(data_graph2019_all_country_source);\nscaleBarGraph(data_graph2019_all_country_source, 32);\nPlotly.newPlot('myplot_graph2019_all_country_source', data_graph2019_all_country_source, {barmode: 'stack', margin: { l: 30, r: 0, b: 100, t: 20, pad: 4 }});\n<\/script><\/p>\n<p>Through the years, the US has always published much more than everyone else and now the pace has accelerated even more. China is also trying to match this and has increased the lead over all the others by quite a bit. The UK is in a respectable third position.<\/p>\n<div id=\"myplot_graph2019_all_country_year\" style=\"height:550px;\"><\/div>\n<p><script>\nvar trace_graph2019_all_country_year_0 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [864, 912, 1083, 1138, 1399, 1537, 2434, 3108, ],\n  text: \"USA\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_country_year_1 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [190, 161, 264, 299, 440, 461, 919, 1248, ],\n  text: \"China\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_country_year_2 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [126, 132, 201, 214, 297, 316, 479, 569, ],\n  text: \"UK\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_country_year_3 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [147, 112, 131, 132, 169, 177, 238, 294, ],\n  text: \"Germany\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_country_year_4 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [106, 93, 121, 121, 131, 125, 197, 279, ],\n  text: \"Canada\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_country_year_5 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [80, 60, 77, 87, 108, 106, 142, 172, ],\n  text: \"Japan\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_country_year_6 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [82, 68, 88, 86, 98, 107, 136, 158, ],\n  text: \"France\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_country_year_7 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [63, 35, 74, 80, 87, 101, 119, 144, ],\n  text: \"Australia\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_country_year_8 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [63, 46, 58, 64, 82, 78, 129, 144, ],\n  text: \"Singapore\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_country_year_9 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [50, 56, 55, 60, 68, 75, 104, 125, ],\n  text: \"Israel\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_country_year_10 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [35, 35, 38, 39, 51, 80, 123, 135, ],\n  text: \"Switzerland\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_country_year_11 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [34, 44, 49, 47, 58, 70, 79, 62, ],\n  text: \"Italy\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_country_year_12 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [30, 23, 31, 37, 56, 62, 94, 101, ],\n  text: \"India\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_country_year_13 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [21, 16, 34, 24, 57, 49, 66, 76, ],\n  text: \"Netherlands\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_country_year_14 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [11, 9, 11, 24, 28, 24, 62, 94, ],\n  text: \"South Korea\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_country_year_15 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [28, 19, 33, 23, 36, 23, 40, 39, ],\n  text: \"Spain\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_country_year_16 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [6, 13, 14, 18, 24, 23, 36, 41, ],\n  text: \"Denmark\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_country_year_17 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [24, 23, 17, 17, 17, 15, 24, 21, ],\n  text: \"Sweden\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_country_year_18 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [12, 12, 17, 11, 19, 19, 19, 9, ],\n  text: \"Ireland\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_country_year_19 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [8, 2, 14, 7, 10, 11, 32, 34, ],\n  text: \"Taiwan\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_country_year_20 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [11, 9, 13, 18, 16, 16, 11, 16, ],\n  text: \"Qatar\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_country_year_21 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [6, 9, 15, 7, 10, 15, 12, 27, ],\n  text: \"Finland\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_country_year_22 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [12, 8, 15, 12, 6, 11, 14, 18, ],\n  text: \"Austria\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_country_year_23 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [3, 7, 5, 5, 13, 9, 23, 31, ],\n  text: \"Russia\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_country_year_24 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [9, 9, 7, 2, 15, 12, 20, 14, ],\n  text: \"Belgium\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_country_year_25 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [9, 3, 4, 10, 8, 10, 14, 22, ],\n  text: \"Poland\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_country_year_26 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [8, 8, 5, 8, 8, 7, 8, 16, ],\n  text: \"Czech Republic\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_country_year_27 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [8, 7, 12, 8, 6, 5, 6, 12, ],\n  text: \"Greece\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_country_year_28 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [3, 4, 6, 8, 8, 2, 13, 8, ],\n  text: \"Portugal\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_country_year_29 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [5, 5, 2, 4, 8, 3, 3, 13, ],\n  text: \"Iran\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_country_year_30 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [1, 3, 2, 5, 5, 7, 8, 10, ],\n  text: \"Brazil\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_country_year_31 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [4, 4, 2, 2, 4, 4, 11, 8, ],\n  text: \"Norway\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_country_year_32 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [0, 1, 2, 5, 1, 4, 11, 14, ],\n  text: \"Hong Kong\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_country_year_33 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [4, 4, 5, 1, 9, 2, 6, 5, ],\n  text: \"Turkey\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_country_year_34 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [1, 0, 4, 3, 4, 2, 5, 13, ],\n  text: \"Saudi Arabia\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_country_year_35 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [1, 2, 1, 1, 0, 1, 6, 18, ],\n  text: \"Macau\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_country_year_36 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [1, 1, 6, 1, 4, 2, 6, 8, ],\n  text: \"Romania\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_country_year_37 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [3, 0, 2, 3, 4, 3, 6, 5, ],\n  text: \"UAE\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_country_year_38 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [1, 1, 1, 2, 3, 5, 2, 7, ],\n  text: \"New Zealand\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_country_year_39 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [0, 3, 1, 3, 1, 3, 4, 3, ],\n  text: \"Chile\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_country_year_40 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [2, 3, 6, 1, 1, 2, 0, 2, ],\n  text: \"Hungary\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_country_year_41 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [0, 1, 1, 1, 1, 1, 6, 3, ],\n  text: \"South Africa\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_country_year_42 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [2, 4, 1, 2, 1, 1, 2, 0, ],\n  text: \"Croatia\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_country_year_43 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [3, 1, 5, 1, 0, 1, 1, 0, ],\n  text: \"Egypt\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2019_all_country_year_44 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", ],\n  y: [1, 0, 3, 1, 0, 1, 4, 2, ],\n  text: \"Vietnam\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar data_graph2019_all_country_year = [trace_graph2019_all_country_year_0, trace_graph2019_all_country_year_1, trace_graph2019_all_country_year_2, trace_graph2019_all_country_year_3, trace_graph2019_all_country_year_4, trace_graph2019_all_country_year_5, trace_graph2019_all_country_year_6, trace_graph2019_all_country_year_7, trace_graph2019_all_country_year_8, trace_graph2019_all_country_year_9, trace_graph2019_all_country_year_10, trace_graph2019_all_country_year_11, trace_graph2019_all_country_year_12, trace_graph2019_all_country_year_13, trace_graph2019_all_country_year_14, trace_graph2019_all_country_year_15, trace_graph2019_all_country_year_16, trace_graph2019_all_country_year_17, trace_graph2019_all_country_year_18, trace_graph2019_all_country_year_19, trace_graph2019_all_country_year_20, trace_graph2019_all_country_year_21, trace_graph2019_all_country_year_22, trace_graph2019_all_country_year_23, trace_graph2019_all_country_year_24, trace_graph2019_all_country_year_25, trace_graph2019_all_country_year_26, trace_graph2019_all_country_year_27, trace_graph2019_all_country_year_28, trace_graph2019_all_country_year_29, trace_graph2019_all_country_year_30, trace_graph2019_all_country_year_31, trace_graph2019_all_country_year_32, trace_graph2019_all_country_year_33, trace_graph2019_all_country_year_34, trace_graph2019_all_country_year_35, trace_graph2019_all_country_year_36, trace_graph2019_all_country_year_37, trace_graph2019_all_country_year_38, trace_graph2019_all_country_year_39, trace_graph2019_all_country_year_40, trace_graph2019_all_country_year_41, trace_graph2019_all_country_year_42, trace_graph2019_all_country_year_43, trace_graph2019_all_country_year_44];\nfixLineGraph(data_graph2019_all_country_year);\nscaleLineGraph(data_graph2019_all_country_year, 16);\nPlotly.newPlot('myplot_graph2019_all_country_year', data_graph2019_all_country_year, {margin: { l: 30, r: 0, b: 100, t: 0, pad: 4 }, showlegend: false, showticklabels: true, xaxis:{autotick: false}});\n<\/script><\/p>\n<h4>USA<\/h4>\n<p>We can also take a look at the 2019 publishing statistics of individual organizations within each country\/continent. Given how much the US publishes, the graph here looks sort of similar to the overall statistics, with Google in the lead.<\/p>\n<div id=\"myplot_graph2019_last_country_org_source_1\" style=\"height:500px;\"><\/div>\n<p><script>\nvar trace_graph2019_last_country_org_source_1_0 = {  x: [\"Google\", \"Microsoft\", \"Carnegie Mellon University\", \"Massachusetts Institute of Technology\", \"Stanford University\", \"Facebook\", \"IBM\", \"UC Berkeley\", \"University of Washington\", \"Amazon\", \"University of Illinois\", \"Allen Institute for Artificial Intelligence\", \"University of California Los Angeles\", \"Cornell University\", \"University of Southern California\", \"Princeton University\", \"Harvard University\", \"Johns Hopkins University\", \"Columbia University\", \"Georgia Institute of Technology\", \"University of Texas at Austin\", \"New York University\", \"Duke University\", \"University of Maryland\", \"University of Pennsylvania\", \"University of Michigan\", \"University of California San Diego\", \"University of Massachusetts Amherst\", \"Rutgers University\", \"Yale University\", \"Toyota Technological Institute at Chicago\", \"Purdue University\", \"University of California Santa Barbara\", \"Salesforce Research\", \"University of Wisconsin\u2013Madison\", \"University of Chicago\", \"Uber AI Labs\", \"Boston University\", \"The Ohio State University\", \"Texas A&M University\", \"University of Minnesota\", \"UC Irvine\", \"Rice University\", \"University of Illinois at Chicago\", \"Adobe Research\", ],\n  y: [23, 57, 37, 21, 16, 9, 33, 16, 7, 7, 11, 3, 15, 10, 18, 1, 6, 6, 3, 6, 10, 1, 8, 10, 1, 12, 3, 1, 10, 8, 0, 7, 3, 0, 2, 3, 3, 1, 3, 7, 3, 3, 5, 6, 3, ],\n  name: 'AAAI',\n  type: 'bar'\n};\nvar trace_graph2019_last_country_org_source_1_1 = {  x: [\"Google\", \"Microsoft\", \"Carnegie Mellon University\", \"Massachusetts Institute of Technology\", \"Stanford University\", \"Facebook\", \"IBM\", \"UC Berkeley\", \"University of Washington\", \"Amazon\", \"University of Illinois\", \"Allen Institute for Artificial Intelligence\", \"University of California Los Angeles\", \"Cornell University\", \"University of Southern California\", \"Princeton University\", \"Harvard University\", \"Johns Hopkins University\", \"Columbia University\", \"Georgia Institute of Technology\", \"University of Texas at Austin\", \"New York University\", \"Duke University\", \"University of Maryland\", \"University of Pennsylvania\", \"University of Michigan\", \"University of California San Diego\", \"University of Massachusetts Amherst\", \"Rutgers University\", \"Yale University\", \"Toyota Technological Institute at Chicago\", \"Purdue University\", \"University of California Santa Barbara\", \"Salesforce Research\", \"University of Wisconsin\u2013Madison\", \"University of Chicago\", \"Uber AI Labs\", \"Boston University\", \"The Ohio State University\", \"Texas A&M University\", \"University of Minnesota\", \"UC Irvine\", \"Rice University\", \"University of Illinois at Chicago\", \"Adobe Research\", ],\n  y: [61, 30, 32, 42, 21, 28, 14, 27, 7, 3, 12, 3, 17, 3, 4, 11, 4, 2, 3, 13, 5, 12, 7, 1, 5, 2, 3, 4, 0, 2, 1, 2, 1, 6, 0, 4, 4, 2, 1, 1, 3, 4, 8, 1, 2, ],\n  name: 'ICLR',\n  type: 'bar'\n};\nvar trace_graph2019_last_country_org_source_1_2 = {  x: [\"Google\", \"Microsoft\", \"Carnegie Mellon University\", \"Massachusetts Institute of Technology\", \"Stanford University\", \"Facebook\", \"IBM\", \"UC Berkeley\", \"University of Washington\", \"Amazon\", \"University of Illinois\", \"Allen Institute for Artificial Intelligence\", \"University of California Los Angeles\", \"Cornell University\", \"University of Southern California\", \"Princeton University\", \"Harvard University\", \"Johns Hopkins University\", \"Columbia University\", \"Georgia Institute of Technology\", \"University of Texas at Austin\", \"New York University\", \"Duke University\", \"University of Maryland\", \"University of Pennsylvania\", \"University of Michigan\", \"University of California San Diego\", \"University of Massachusetts Amherst\", \"Rutgers University\", \"Yale University\", \"Toyota Technological Institute at Chicago\", \"Purdue University\", \"University of California Santa Barbara\", \"Salesforce Research\", \"University of Wisconsin\u2013Madison\", \"University of Chicago\", \"Uber AI Labs\", \"Boston University\", \"The Ohio State University\", \"Texas A&M University\", \"University of Minnesota\", \"UC Irvine\", \"Rice University\", \"University of Illinois at Chicago\", \"Adobe Research\", ],\n  y: [93, 31, 29, 40, 31, 25, 20, 35, 14, 7, 8, 1, 6, 14, 6, 19, 14, 8, 7, 12, 12, 7, 11, 7, 6, 5, 2, 4, 3, 6, 5, 4, 0, 3, 3, 5, 6, 6, 0, 2, 4, 1, 2, 2, 5, ],\n  name: 'ICML',\n  type: 'bar'\n};\nvar trace_graph2019_last_country_org_source_1_3 = {  x: [\"Google\", \"Microsoft\", \"Carnegie Mellon University\", \"Massachusetts Institute of Technology\", \"Stanford University\", \"Facebook\", \"IBM\", \"UC Berkeley\", \"University of Washington\", \"Amazon\", \"University of Illinois\", \"Allen Institute for Artificial Intelligence\", \"University of California Los Angeles\", \"Cornell University\", \"University of Southern California\", \"Princeton University\", \"Harvard University\", \"Johns Hopkins University\", \"Columbia University\", \"Georgia Institute of Technology\", \"University of Texas at Austin\", \"New York University\", \"Duke University\", \"University of Maryland\", \"University of Pennsylvania\", \"University of Michigan\", \"University of California San Diego\", \"University of Massachusetts Amherst\", \"Rutgers University\", \"Yale University\", \"Toyota Technological Institute at Chicago\", \"Purdue University\", \"University of California Santa Barbara\", \"Salesforce Research\", \"University of Wisconsin\u2013Madison\", \"University of Chicago\", \"Uber AI Labs\", \"Boston University\", \"The Ohio State University\", \"Texas A&M University\", \"University of Minnesota\", \"UC Irvine\", \"Rice University\", \"University of Illinois at Chicago\", \"Adobe Research\", ],\n  y: [111, 76, 82, 82, 79, 46, 31, 56, 29, 22, 28, 6, 27, 27, 12, 37, 24, 8, 43, 24, 22, 25, 23, 11, 17, 10, 13, 10, 13, 9, 8, 10, 4, 3, 19, 8, 11, 11, 7, 9, 12, 4, 7, 0, 2, ],\n  name: 'NeurIPS',\n  type: 'bar'\n};\nvar trace_graph2019_last_country_org_source_1_4 = {  x: [\"Google\", \"Microsoft\", \"Carnegie Mellon University\", \"Massachusetts Institute of Technology\", \"Stanford University\", \"Facebook\", \"IBM\", \"UC Berkeley\", \"University of Washington\", \"Amazon\", \"University of Illinois\", \"Allen Institute for Artificial Intelligence\", \"University of California Los Angeles\", \"Cornell University\", \"University of Southern California\", \"Princeton University\", \"Harvard University\", \"Johns Hopkins University\", \"Columbia University\", \"Georgia Institute of Technology\", \"University of Texas at Austin\", \"New York University\", \"Duke University\", \"University of Maryland\", \"University of Pennsylvania\", \"University of Michigan\", \"University of California San Diego\", \"University of Massachusetts Amherst\", \"Rutgers University\", \"Yale University\", \"Toyota Technological Institute at Chicago\", \"Purdue University\", \"University of California Santa Barbara\", \"Salesforce Research\", \"University of Wisconsin\u2013Madison\", \"University of Chicago\", \"Uber AI Labs\", \"Boston University\", \"The Ohio State University\", \"Texas A&M University\", \"University of Minnesota\", \"UC Irvine\", \"Rice University\", \"University of Illinois at Chicago\", \"Adobe Research\", ],\n  y: [1, 2, 1, 0, 1, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ],\n  name: 'CL',\n  type: 'bar'\n};\nvar trace_graph2019_last_country_org_source_1_5 = {  x: [\"Google\", \"Microsoft\", \"Carnegie Mellon University\", \"Massachusetts Institute of Technology\", \"Stanford University\", \"Facebook\", \"IBM\", \"UC Berkeley\", \"University of Washington\", \"Amazon\", \"University of Illinois\", \"Allen Institute for Artificial Intelligence\", \"University of California Los Angeles\", \"Cornell University\", \"University of Southern California\", \"Princeton University\", \"Harvard University\", \"Johns Hopkins University\", \"Columbia University\", \"Georgia Institute of Technology\", \"University of Texas at Austin\", \"New York University\", \"Duke University\", \"University of Maryland\", \"University of Pennsylvania\", \"University of Michigan\", \"University of California San Diego\", \"University of Massachusetts Amherst\", \"Rutgers University\", \"Yale University\", \"Toyota Technological Institute at Chicago\", \"Purdue University\", \"University of California Santa Barbara\", \"Salesforce Research\", \"University of Wisconsin\u2013Madison\", \"University of Chicago\", \"Uber AI Labs\", \"Boston University\", \"The Ohio State University\", \"Texas A&M University\", \"University of Minnesota\", \"UC Irvine\", \"Rice University\", \"University of Illinois at Chicago\", \"Adobe Research\", ],\n  y: [2, 1, 1, 2, 2, 4, 3, 0, 2, 0, 0, 3, 1, 1, 0, 0, 1, 3, 0, 0, 0, 2, 0, 1, 2, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ],\n  name: 'TACL',\n  type: 'bar'\n};\nvar trace_graph2019_last_country_org_source_1_6 = {  x: [\"Google\", \"Microsoft\", \"Carnegie Mellon University\", \"Massachusetts Institute of Technology\", \"Stanford University\", \"Facebook\", \"IBM\", \"UC Berkeley\", \"University of Washington\", \"Amazon\", \"University of Illinois\", \"Allen Institute for Artificial Intelligence\", \"University of California Los Angeles\", \"Cornell University\", \"University of Southern California\", \"Princeton University\", \"Harvard University\", \"Johns Hopkins University\", \"Columbia University\", \"Georgia Institute of Technology\", \"University of Texas at Austin\", \"New York University\", \"Duke University\", \"University of Maryland\", \"University of Pennsylvania\", \"University of Michigan\", \"University of California San Diego\", \"University of Massachusetts Amherst\", \"Rutgers University\", \"Yale University\", \"Toyota Technological Institute at Chicago\", \"Purdue University\", \"University of California Santa Barbara\", \"Salesforce Research\", \"University of Wisconsin\u2013Madison\", \"University of Chicago\", \"Uber AI Labs\", \"Boston University\", \"The Ohio State University\", \"Texas A&M University\", \"University of Minnesota\", \"UC Irvine\", \"Rice University\", \"University of Illinois at Chicago\", \"Adobe Research\", ],\n  y: [6, 2, 5, 2, 3, 1, 1, 0, 4, 1, 2, 4, 2, 2, 5, 1, 0, 3, 0, 0, 1, 0, 0, 0, 4, 1, 0, 1, 0, 0, 2, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, ],\n  name: 'CoNLL',\n  type: 'bar'\n};\nvar trace_graph2019_last_country_org_source_1_7 = {  x: [\"Google\", \"Microsoft\", \"Carnegie Mellon University\", \"Massachusetts Institute of Technology\", \"Stanford University\", \"Facebook\", \"IBM\", \"UC Berkeley\", \"University of Washington\", \"Amazon\", \"University of Illinois\", \"Allen Institute for Artificial Intelligence\", \"University of California Los Angeles\", \"Cornell University\", \"University of Southern California\", \"Princeton University\", \"Harvard University\", \"Johns Hopkins University\", \"Columbia University\", \"Georgia Institute of Technology\", \"University of Texas at Austin\", \"New York University\", \"Duke University\", \"University of Maryland\", \"University of Pennsylvania\", \"University of Michigan\", \"University of California San Diego\", \"University of Massachusetts Amherst\", \"Rutgers University\", \"Yale University\", \"Toyota Technological Institute at Chicago\", \"Purdue University\", \"University of California Santa Barbara\", \"Salesforce Research\", \"University of Wisconsin\u2013Madison\", \"University of Chicago\", \"Uber AI Labs\", \"Boston University\", \"The Ohio State University\", \"Texas A&M University\", \"University of Minnesota\", \"UC Irvine\", \"Rice University\", \"University of Illinois at Chicago\", \"Adobe Research\", ],\n  y: [21, 25, 31, 12, 11, 14, 18, 3, 19, 22, 5, 25, 3, 4, 7, 0, 12, 15, 5, 3, 3, 3, 3, 7, 6, 2, 2, 4, 3, 1, 5, 2, 5, 3, 1, 1, 0, 0, 4, 2, 0, 4, 0, 2, 1, ],\n  name: 'NAACL',\n  type: 'bar'\n};\nvar trace_graph2019_last_country_org_source_1_8 = {  x: [\"Google\", \"Microsoft\", \"Carnegie Mellon University\", \"Massachusetts Institute of Technology\", \"Stanford University\", \"Facebook\", \"IBM\", \"UC Berkeley\", \"University of Washington\", \"Amazon\", \"University of Illinois\", \"Allen Institute for Artificial Intelligence\", \"University of California Los Angeles\", \"Cornell University\", \"University of Southern California\", \"Princeton University\", \"Harvard University\", \"Johns Hopkins University\", \"Columbia University\", \"Georgia Institute of Technology\", \"University of Texas at Austin\", \"New York University\", \"Duke University\", \"University of Maryland\", \"University of Pennsylvania\", \"University of Michigan\", \"University of California San Diego\", \"University of Massachusetts Amherst\", \"Rutgers University\", \"Yale University\", \"Toyota Technological Institute at Chicago\", \"Purdue University\", \"University of California Santa Barbara\", \"Salesforce Research\", \"University of Wisconsin\u2013Madison\", \"University of Chicago\", \"Uber AI Labs\", \"Boston University\", \"The Ohio State University\", \"Texas A&M University\", \"University of Minnesota\", \"UC Irvine\", \"Rice University\", \"University of Illinois at Chicago\", \"Adobe Research\", ],\n  y: [27, 47, 34, 9, 11, 25, 19, 3, 22, 24, 16, 29, 8, 9, 16, 1, 5, 12, 7, 3, 4, 5, 2, 10, 6, 4, 8, 4, 2, 2, 5, 2, 6, 7, 1, 4, 1, 3, 3, 2, 1, 4, 0, 5, 2, ],\n  name: 'EMNLP',\n  type: 'bar'\n};\nvar trace_graph2019_last_country_org_source_1_9 = {  x: [\"Google\", \"Microsoft\", \"Carnegie Mellon University\", \"Massachusetts Institute of Technology\", \"Stanford University\", \"Facebook\", \"IBM\", \"UC Berkeley\", \"University of Washington\", \"Amazon\", \"University of Illinois\", \"Allen Institute for Artificial Intelligence\", \"University of California Los Angeles\", \"Cornell University\", \"University of Southern California\", \"Princeton University\", \"Harvard University\", \"Johns Hopkins University\", \"Columbia University\", \"Georgia Institute of Technology\", \"University of Texas at Austin\", \"New York University\", \"Duke University\", \"University of Maryland\", \"University of Pennsylvania\", \"University of Michigan\", \"University of California San Diego\", \"University of Massachusetts Amherst\", \"Rutgers University\", \"Yale University\", \"Toyota Technological Institute at Chicago\", \"Purdue University\", \"University of California Santa Barbara\", \"Salesforce Research\", \"University of Wisconsin\u2013Madison\", \"University of Chicago\", \"Uber AI Labs\", \"Boston University\", \"The Ohio State University\", \"Texas A&M University\", \"University of Minnesota\", \"UC Irvine\", \"Rice University\", \"University of Illinois at Chicago\", \"Adobe Research\", ],\n  y: [36, 41, 36, 10, 9, 24, 19, 6, 22, 20, 7, 15, 4, 6, 5, 1, 4, 13, 1, 1, 3, 5, 4, 6, 4, 11, 5, 7, 1, 4, 6, 3, 10, 6, 1, 0, 0, 1, 6, 1, 0, 3, 0, 3, 3, ],\n  name: 'ACL',\n  type: 'bar'\n};\nvar data_graph2019_last_country_org_source_1 = [trace_graph2019_last_country_org_source_1_0, trace_graph2019_last_country_org_source_1_1, trace_graph2019_last_country_org_source_1_2, trace_graph2019_last_country_org_source_1_3, trace_graph2019_last_country_org_source_1_4, trace_graph2019_last_country_org_source_1_5, trace_graph2019_last_country_org_source_1_6, trace_graph2019_last_country_org_source_1_7, trace_graph2019_last_country_org_source_1_8, trace_graph2019_last_country_org_source_1_9];\nfixBarGraph(data_graph2019_last_country_org_source_1);\nscaleBarGraph(data_graph2019_last_country_org_source_1, 32);\nPlotly.newPlot('myplot_graph2019_last_country_org_source_1', data_graph2019_last_country_org_source_1, {barmode: 'stack', margin: { l: 30, r: 0, b: 270, t: 20, pad: 4 }});\n<\/script><\/p>\n<h4>China<\/h4>\n<p>In China, Tsinghua and Peking universities distinctly stand out in terms of publication numbers. The other top ranks are also mostly held by universities, with Baidu and Alibaba being the main industry publishers.<\/p>\n<div id=\"myplot_graph2019_last_country_org_source_2\" style=\"height:500px;\"><\/div>\n<p><script>\nvar trace_graph2019_last_country_org_source_2_0 = {  x: [\"Tsinghua University\", \"Peking University\", \"University of Chinese Academy of Sciences\", \"Chinese Academy of Sciences\", \"Nanjing University\", \"The Chinese University of Hong Kong\", \"Zhejiang University\", \"University of Science and Technology of China\", \"Beihang University\", \"Baidu\", \"Fudan University\", \"Shanghai Jiao Tong University\", \"Northeastern University\", \"Harbin Institute of Technology\", \"Sun Yat-sen University\", \"Soochow University\", \"Beijing University of Posts and Telecommunications\", \"Alibaba DAMO Academy\", \"National University of Defense Technology\", \"Hong Kong University of Science and Technology\", \"East China Normal University\", \"Beijing Institute of Big Data Research\", \"Tianjin University\", \"Xiamen University\", \"Beijing Institute of Technology\", \"Xi'an Jiaotong University\", \"Xidian University\", \"Westlake University\", \"University of Hong Kong\", \"Shenzhen Institutes of Advanced Technology\", \"JD AI Research\", \"Nankai University\", \"South China University of Technology\", \"Huazhong University of Science and Technology\", \"Shanghai Tech University\", \"Beijing Jiaotong University\", \"Southeast University\", \"Wuhan University\", \"Renmin University of China\", \"The Hong Kong Polytechnic University\", \"Alibaba AI Labs\", \"Wangxuan Institute of Computer Technology\", \"Shanghai University of Finance and Economics\", \"Dalian University of Technology\", \"Shanghai Institute of Intelligent Electronics & Systems\", ],\n  y: [60, 40, 43, 42, 36, 22, 24, 31, 30, 16, 25, 22, 4, 12, 17, 5, 7, 5, 13, 5, 10, 0, 8, 10, 6, 6, 9, 1, 5, 7, 8, 2, 5, 6, 4, 4, 8, 5, 3, 3, 6, 0, 5, 5, 7, ],\n  name: 'AAAI',\n  type: 'bar'\n};\nvar trace_graph2019_last_country_org_source_2_1 = {  x: [\"Tsinghua University\", \"Peking University\", \"University of Chinese Academy of Sciences\", \"Chinese Academy of Sciences\", \"Nanjing University\", \"The Chinese University of Hong Kong\", \"Zhejiang University\", \"University of Science and Technology of China\", \"Beihang University\", \"Baidu\", \"Fudan University\", \"Shanghai Jiao Tong University\", \"Northeastern University\", \"Harbin Institute of Technology\", \"Sun Yat-sen University\", \"Soochow University\", \"Beijing University of Posts and Telecommunications\", \"Alibaba DAMO Academy\", \"National University of Defense Technology\", \"Hong Kong University of Science and Technology\", \"East China Normal University\", \"Beijing Institute of Big Data Research\", \"Tianjin University\", \"Xiamen University\", \"Beijing Institute of Technology\", \"Xi'an Jiaotong University\", \"Xidian University\", \"Westlake University\", \"University of Hong Kong\", \"Shenzhen Institutes of Advanced Technology\", \"JD AI Research\", \"Nankai University\", \"South China University of Technology\", \"Huazhong University of Science and Technology\", \"Shanghai Tech University\", \"Beijing Jiaotong University\", \"Southeast University\", \"Wuhan University\", \"Renmin University of China\", \"The Hong Kong Polytechnic University\", \"Alibaba AI Labs\", \"Wangxuan Institute of Computer Technology\", \"Shanghai University of Finance and Economics\", \"Dalian University of Technology\", \"Shanghai Institute of Intelligent Electronics & Systems\", ],\n  y: [12, 10, 2, 2, 1, 5, 2, 4, 0, 5, 1, 6, 4, 0, 0, 0, 0, 0, 0, 4, 1, 3, 0, 0, 1, 0, 0, 0, 2, 1, 2, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, ],\n  name: 'ICLR',\n  type: 'bar'\n};\nvar trace_graph2019_last_country_org_source_2_2 = {  x: [\"Tsinghua University\", \"Peking University\", \"University of Chinese Academy of Sciences\", \"Chinese Academy of Sciences\", \"Nanjing University\", \"The Chinese University of Hong Kong\", \"Zhejiang University\", \"University of Science and Technology of China\", \"Beihang University\", \"Baidu\", \"Fudan University\", \"Shanghai Jiao Tong University\", \"Northeastern University\", \"Harbin Institute of Technology\", \"Sun Yat-sen University\", \"Soochow University\", \"Beijing University of Posts and Telecommunications\", \"Alibaba DAMO Academy\", \"National University of Defense Technology\", \"Hong Kong University of Science and Technology\", \"East China Normal University\", \"Beijing Institute of Big Data Research\", \"Tianjin University\", \"Xiamen University\", \"Beijing Institute of Technology\", \"Xi'an Jiaotong University\", \"Xidian University\", \"Westlake University\", \"University of Hong Kong\", \"Shenzhen Institutes of Advanced Technology\", \"JD AI Research\", \"Nankai University\", \"South China University of Technology\", \"Huazhong University of Science and Technology\", \"Shanghai Tech University\", \"Beijing Jiaotong University\", \"Southeast University\", \"Wuhan University\", \"Renmin University of China\", \"The Hong Kong Polytechnic University\", \"Alibaba AI Labs\", \"Wangxuan Institute of Computer Technology\", \"Shanghai University of Finance and Economics\", \"Dalian University of Technology\", \"Shanghai Institute of Intelligent Electronics & Systems\", ],\n  y: [11, 9, 1, 4, 5, 4, 2, 0, 0, 0, 3, 3, 2, 0, 1, 0, 0, 1, 0, 2, 2, 2, 1, 0, 1, 1, 2, 0, 3, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, ],\n  name: 'ICML',\n  type: 'bar'\n};\nvar trace_graph2019_last_country_org_source_2_3 = {  x: [\"Tsinghua University\", \"Peking University\", \"University of Chinese Academy of Sciences\", \"Chinese Academy of Sciences\", \"Nanjing University\", \"The Chinese University of Hong Kong\", \"Zhejiang University\", \"University of Science and Technology of China\", \"Beihang University\", \"Baidu\", \"Fudan University\", \"Shanghai Jiao Tong University\", \"Northeastern University\", \"Harbin Institute of 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Economics\", \"Dalian University of Technology\", \"Shanghai Institute of Intelligent Electronics & Systems\", ],\n  y: [34, 25, 14, 7, 11, 8, 9, 6, 2, 9, 7, 3, 14, 2, 2, 0, 1, 1, 2, 5, 1, 1, 4, 3, 2, 5, 3, 0, 0, 1, 1, 3, 4, 1, 1, 0, 0, 1, 0, 2, 0, 1, 2, 2, 0, ],\n  name: 'NeurIPS',\n  type: 'bar'\n};\nvar trace_graph2019_last_country_org_source_2_4 = {  x: [\"Tsinghua University\", \"Peking University\", \"University of Chinese Academy of Sciences\", \"Chinese Academy of Sciences\", \"Nanjing University\", \"The Chinese University of Hong Kong\", \"Zhejiang University\", \"University of Science and Technology of China\", \"Beihang University\", \"Baidu\", \"Fudan University\", \"Shanghai Jiao Tong University\", \"Northeastern University\", \"Harbin Institute of Technology\", \"Sun Yat-sen University\", \"Soochow University\", \"Beijing University of Posts and Telecommunications\", \"Alibaba DAMO Academy\", \"National University of Defense Technology\", \"Hong Kong University of 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0, 0, 0, 0, 0, 0, 0, 0, 0, ],\n  name: 'CL',\n  type: 'bar'\n};\nvar trace_graph2019_last_country_org_source_2_5 = {  x: [\"Tsinghua University\", \"Peking University\", \"University of Chinese Academy of Sciences\", \"Chinese Academy of Sciences\", \"Nanjing University\", \"The Chinese University of Hong Kong\", \"Zhejiang University\", \"University of Science and Technology of China\", \"Beihang University\", \"Baidu\", \"Fudan University\", \"Shanghai Jiao Tong University\", \"Northeastern University\", \"Harbin Institute of Technology\", \"Sun Yat-sen University\", \"Soochow University\", \"Beijing University of Posts and Telecommunications\", \"Alibaba DAMO Academy\", \"National University of Defense Technology\", \"Hong Kong University of Science and Technology\", \"East China Normal University\", \"Beijing Institute of Big Data Research\", \"Tianjin University\", \"Xiamen University\", \"Beijing Institute of Technology\", \"Xi'an Jiaotong University\", \"Xidian University\", 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\"Shanghai Tech University\", \"Beijing Jiaotong University\", \"Southeast University\", \"Wuhan University\", \"Renmin University of China\", \"The Hong Kong Polytechnic University\", \"Alibaba AI Labs\", \"Wangxuan Institute of Computer Technology\", \"Shanghai University of Finance and Economics\", \"Dalian University of Technology\", \"Shanghai Institute of Intelligent Electronics & Systems\", ],\n  y: [0, 2, 4, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 3, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, ],\n  name: 'CoNLL',\n  type: 'bar'\n};\nvar trace_graph2019_last_country_org_source_2_7 = {  x: [\"Tsinghua University\", \"Peking University\", \"University of Chinese Academy of Sciences\", \"Chinese Academy of Sciences\", \"Nanjing University\", \"The Chinese University of Hong Kong\", \"Zhejiang University\", \"University of Science and Technology of China\", \"Beihang University\", \"Baidu\", \"Fudan University\", \"Shanghai Jiao Tong 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10, 2, 2, 1, 3, 3, 3, 4, 2, 0, 4, 1, 3, 0, 3, 2, 0, 2, 4, 2, 1, 2, 3, 2, 7, 0, 1, 0, ],\n  name: 'EMNLP',\n  type: 'bar'\n};\nvar trace_graph2019_last_country_org_source_2_9 = {  x: [\"Tsinghua University\", \"Peking University\", \"University of Chinese Academy of Sciences\", \"Chinese Academy of Sciences\", \"Nanjing University\", \"The Chinese University of Hong Kong\", \"Zhejiang University\", \"University of Science and Technology of China\", \"Beihang University\", \"Baidu\", \"Fudan University\", \"Shanghai Jiao Tong University\", \"Northeastern University\", \"Harbin Institute of Technology\", \"Sun Yat-sen University\", \"Soochow University\", \"Beijing University of Posts and Telecommunications\", \"Alibaba DAMO Academy\", \"National University of Defense Technology\", \"Hong Kong University of Science and Technology\", \"East China Normal University\", \"Beijing Institute of Big Data Research\", \"Tianjin University\", \"Xiamen University\", \"Beijing Institute of 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trace_graph2019_last_country_org_source_2_1, trace_graph2019_last_country_org_source_2_2, trace_graph2019_last_country_org_source_2_3, trace_graph2019_last_country_org_source_2_4, trace_graph2019_last_country_org_source_2_5, trace_graph2019_last_country_org_source_2_6, trace_graph2019_last_country_org_source_2_7, trace_graph2019_last_country_org_source_2_8, trace_graph2019_last_country_org_source_2_9];\nfixBarGraph(data_graph2019_last_country_org_source_2);\nscaleBarGraph(data_graph2019_last_country_org_source_2, 32);\nPlotly.newPlot('myplot_graph2019_last_country_org_source_2', data_graph2019_last_country_org_source_2, {barmode: 'stack', margin: { l: 30, r: 0, b: 270, t: 20, pad: 4 }});\n<\/script><\/p>\n<h4>UK<\/h4>\n<p>In the UK, DeepMind takes the lead. They are followed by Cambridge, Oxford, Edinburgh, UCL, Imperial and the Alan Turing Institute. Worth noting that the Turing institute is largely virtual, so academic there often have affiliations with other universities as well. Out of the top 7, Cambridge and Edinburgh publish quite a bit in NLP, while the others are focusing mainly on ML.<\/p>\n<div id=\"myplot_graph2019_last_country_org_source_3\" style=\"height:550px;\"><\/div>\n<p><script>\nvar trace_graph2019_last_country_org_source_3_0 = {  x: [\"DeepMind\", \"University of Cambridge\", \"University of Oxford\", \"University of Edinburgh\", \"University College London\", \"Imperial College London\", \"Alan Turing Institute\", \"University of Sheffield\", \"Queen Mary University of London\", \"University of Manchester\", \"Lancaster University\", \"University of Warwick\", \"King's College London\", \"Cardiff University\", \"University of Southampton\", \"University of Bristol\", \"The ALTA Institute\", \"University of Essex\", \"University of Nottingham\", \"University of Birmingham\", \"Heriot Watt University\", \"University of Liverpool\", \"University of Kent\", \"University of London\", \"University of Bath\", \"University of Aberdeen\", \"Queen's 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1, 0, 0, ],\n  name: 'NeurIPS',\n  type: 'bar'\n};\nvar trace_graph2019_last_country_org_source_3_4 = {  x: [\"DeepMind\", \"University of Cambridge\", \"University of Oxford\", \"University of Edinburgh\", \"University College London\", \"Imperial College London\", \"Alan Turing Institute\", \"University of Sheffield\", \"Queen Mary University of London\", \"University of Manchester\", \"Lancaster University\", \"University of Warwick\", \"King's College London\", \"Cardiff University\", \"University of Southampton\", \"University of Bristol\", \"The ALTA Institute\", \"University of Essex\", \"University of Nottingham\", \"University of Birmingham\", \"Heriot Watt University\", \"University of Liverpool\", \"University of Kent\", \"University of London\", \"University of Bath\", \"University of Aberdeen\", \"Queen's University Belfast\", \"Newcastle University\", \"University of Leeds\", \"University of Wolverhampton\", \"University of Exeter\", \"Bournemouth University\", 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\"FiveAI\", \"University of Lincoln\", \"University of Surrey\", \"University of Sussex\", \"Plumerai Research\", \"Invenia Labs\", \"Open University\", \"University of St Andrews\", ],\n  y: [6, 14, 4, 21, 3, 5, 2, 2, 0, 4, 0, 4, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ],\n  name: 'EMNLP',\n  type: 'bar'\n};\nvar trace_graph2019_last_country_org_source_3_9 = {  x: [\"DeepMind\", \"University of Cambridge\", \"University of Oxford\", \"University of Edinburgh\", \"University College London\", \"Imperial College London\", \"Alan Turing Institute\", \"University of Sheffield\", \"Queen Mary University of London\", \"University of Manchester\", \"Lancaster University\", \"University of Warwick\", \"King's College London\", \"Cardiff University\", \"University of Southampton\", \"University of Bristol\", \"The ALTA Institute\", \"University of Essex\", \"University of Nottingham\", \"University of Birmingham\", \"Heriot Watt 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trace_graph2019_last_country_org_source_3_2, trace_graph2019_last_country_org_source_3_3, trace_graph2019_last_country_org_source_3_4, trace_graph2019_last_country_org_source_3_5, trace_graph2019_last_country_org_source_3_6, trace_graph2019_last_country_org_source_3_7, trace_graph2019_last_country_org_source_3_8, trace_graph2019_last_country_org_source_3_9];\nfixBarGraph(data_graph2019_last_country_org_source_3);\nscaleBarGraph(data_graph2019_last_country_org_source_3, 32);\nPlotly.newPlot('myplot_graph2019_last_country_org_source_3', data_graph2019_last_country_org_source_3, {barmode: 'stack', margin: { l: 30, r: 0, b: 220, t: 20, pad: 4 }});\n<\/script><\/p>\n<h4>Germany<\/h4>\n<p>In Germany, Darmstadt is the top publisher, with 2\/3 of the papers in published in the area of NLP. Bosch is putting up some competition, ranking second with mostly ML papers. Saarland, LMU Munich, T\u00fcbingen, TU Munich and the Max Planck Institute for Intelligent Systems are also represented at the top.<\/p>\n<div id=\"myplot_graph2019_last_country_org_source_4\" style=\"height:530px;\"><\/div>\n<p><script>\nvar trace_graph2019_last_country_org_source_4_0 = {  x: [\"Technische Universita\u0308t Darmstadt\", \"Robert Bosch GmbH\", \"Saarland University\", \"University of Munich\", \"University of Tu\u0308bingen\", \"Technical University of Munich\", \"Max Planck Institute for Intelligent Systems\", \"University of Heidelberg\", \"University of Stuttgart\", \"MMax Planck Institute for Informatics\", \"University of Freiburg\", \"University of Mannheim\", \"Max Planck Institute\", \"University of Hamburg\", \"Universita\u0308t Leipzig\", \"Technische Universita\u0308t Dresden\", \"Research Training Group AIPHES\", \"Karlsruhe Institute of Technology KIT\", \"Universit\u00e4t Bremen\", \"Technische Universit\u00e4t Kaiserslautern\", \"Technische 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32);\nPlotly.newPlot('myplot_graph2019_last_country_org_source_4', data_graph2019_last_country_org_source_4, {barmode: 'stack', margin: { l: 30, r: 0, b: 250, t: 20, pad: 4 }});\n<\/script><\/p>\n<h4>Canada<\/h4>\n<p>Among the Canadian organizations, University of Toronto stands out with an impressive publication count. Universite\u0301 de Montre\u0301al and the Vector Institute are also at the top, along with Alberta, McGill, Waterloo, MILA and University of British Columbia. University of Waterloo seems to be the only one with a larger focus on NLP, with the others publishing mostly in the general ML conferences.<\/p>\n<div id=\"myplot_graph2019_last_country_org_source_5\" style=\"height:550px;\"><\/div>\n<p><script>\nvar trace_graph2019_last_country_org_source_5_0 = {  x: [\"University of Toronto\", \"Universite\u0301 de Montre\u0301al\", \"Vector Institute\", \"University of Alberta\", \"McGill University\", \"University of Waterloo\", \"Montreal Institute of Learning Algorithms\", \"University of British Columbia\", \"Simon Fraser University\", \"Canadian Institute for Advanced Research\", \"National Research Council Canada\", \"York University\", \"University of Ottawa\", \"University of Western Ontario\", \"Dalhousie University\", \"McMaster University\", \"University of Regina\", \"University of Calgary\", \"Universit\u00e9 du Qu\u00e9bec en Outaouais\", \"AdeptMind Research\", \"Queen's University\", \"University of Guelph\", \"Wilfrid Laurier 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\"McGill University\", \"University of Waterloo\", \"Montreal Institute of Learning Algorithms\", \"University of British Columbia\", \"Simon Fraser University\", \"Canadian Institute for Advanced Research\", \"National Research Council Canada\", \"York University\", \"University of Ottawa\", \"University of Western Ontario\", \"Dalhousie University\", \"McMaster University\", \"University of Regina\", \"University of Calgary\", \"Universit\u00e9 du Qu\u00e9bec en Outaouais\", \"AdeptMind Research\", \"Queen's University\", \"University of Guelph\", \"Wilfrid Laurier University\", \"University of Manitoba\", \"Concordia University\", \"Ryerson University\", \"Universit\u00e9 du Qu\u00e9bec \u00e0 Montr\u00e9al\", \"Li Ka Shing Knowledge Institute\", \"Winterlight Labs\", \"Sunnybrook Research Institute\", \"Universit\u00e9 Laval\", \"University of Victoria\", ],\n  y: [2, 4, 0, 2, 2, 7, 3, 3, 0, 0, 1, 0, 2, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ],\n  name: 'EMNLP',\n 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}});\n<\/script><\/p>\n<h2>Topic Similarity<\/h2>\n<p>To try and capture a bit about the topics in the papers as well, I ran them through LDA and then visualised the results with t-SNE. Looking at the organizations, it is interesting to see how geographical clusters emerge. Chinese universities are at the top, US mostly on the right, Europe on the left, and industry right in the middle.<\/p>\n<p><a href=\"https:\/\/www.marekrei.com\/blog\/wp-content\/uploads\/2020\/02\/graph_org.png\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/www.marekrei.com\/blog\/wp-content\/uploads\/2020\/02\/graph_org.png\" alt=\"\" width=\"842\" height=\"505\" class=\"aligncenter size-full wp-image-1698\" srcset=\"https:\/\/www.marekrei.com\/blog\/wp-content\/uploads\/2020\/02\/graph_org.png 842w, https:\/\/www.marekrei.com\/blog\/wp-content\/uploads\/2020\/02\/graph_org-150x90.png 150w, https:\/\/www.marekrei.com\/blog\/wp-content\/uploads\/2020\/02\/graph_org-300x180.png 300w, https:\/\/www.marekrei.com\/blog\/wp-content\/uploads\/2020\/02\/graph_org-768x461.png 768w\" sizes=\"auto, (max-width: 842px) 100vw, 842px\" \/><\/a><\/p>\n<p>We can do the same for authors. The closeness in the graph reflects a combination of topic similarity and the frequency of collaboration.<\/p>\n<p><a href=\"https:\/\/www.marekrei.com\/blog\/wp-content\/uploads\/2020\/02\/graph_author.png\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/www.marekrei.com\/blog\/wp-content\/uploads\/2020\/02\/graph_author.png\" alt=\"\" width=\"903\" height=\"501\" class=\"aligncenter size-full wp-image-1699\" srcset=\"https:\/\/www.marekrei.com\/blog\/wp-content\/uploads\/2020\/02\/graph_author.png 903w, https:\/\/www.marekrei.com\/blog\/wp-content\/uploads\/2020\/02\/graph_author-150x83.png 150w, https:\/\/www.marekrei.com\/blog\/wp-content\/uploads\/2020\/02\/graph_author-300x166.png 300w, https:\/\/www.marekrei.com\/blog\/wp-content\/uploads\/2020\/02\/graph_author-768x426.png 768w\" sizes=\"auto, (max-width: 903px) 100vw, 903px\" \/><\/a><\/p>\n<p>Finally, we can do the same for countries. Given that all countries are working on a range of different topics, this graph is likely more representative of their collaboration frequency.<\/p>\n<p><a href=\"https:\/\/www.marekrei.com\/blog\/wp-content\/uploads\/2020\/02\/graph_country.png\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/www.marekrei.com\/blog\/wp-content\/uploads\/2020\/02\/graph_country.png\" alt=\"\" width=\"981\" height=\"555\" class=\"aligncenter size-full wp-image-1700\" srcset=\"https:\/\/www.marekrei.com\/blog\/wp-content\/uploads\/2020\/02\/graph_country.png 981w, https:\/\/www.marekrei.com\/blog\/wp-content\/uploads\/2020\/02\/graph_country-150x85.png 150w, https:\/\/www.marekrei.com\/blog\/wp-content\/uploads\/2020\/02\/graph_country-300x170.png 300w, https:\/\/www.marekrei.com\/blog\/wp-content\/uploads\/2020\/02\/graph_country-768x434.png 768w\" sizes=\"auto, (max-width: 981px) 100vw, 981px\" \/><\/a><\/p>\n<h2>Additional Statistics<\/h2>\n<p>Finally, let's finish off with a couple of fun stats.<\/p>\n<ul>\n<li>Percentage of papers mentioning GitHub (indicating that the code is made available): ACL 70%, EMNLP 69%, NAACL 68%, ICLR 56%, NeurIPS 46%, ICML 45%, AAAI 31%. It seems the NLP papers are releasing their code much more freely.\n<li>Percentage of papers mentioning MNIST: NeurIPS 20%, ICML 25%, ICLR 32%. Seems like it's still a cornerstone of ML research.\n<li>Most authors on a single paper: 24. <a href=\"https:\/\/arxiv.org\/abs\/1909.05378\">CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases<\/a>\n<li>The longest paper title: <a href=\"https:\/\/arxiv.org\/abs\/1809.02719\">What if We Simply Swap the Two Text Fragments? A Straightforward yet Effective Way to Test the Robustness of Methods to Confounding Signals in Nature Language Inference Tasks<\/a>\n<li>The shortest paper title: <a href=\"https:\/\/arxiv.org\/abs\/1905.05178\">Graph U-Nets<\/a>\n<\/ul>\n<p>That's it for this time. Looking forward to all the interesting work that is going to be published in 2020!<\/p>\n<p>The data used in this post can be found <a href=\"https:\/\/github.com\/marekrei\/ml_nlp_paper_data\">here<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>It is about time we once again take a look at the publication statistics of the past year. 2019 was another record breaking year in&hellip;<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-1619","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v23.7 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>ML and NLP Publications in 2019 - Marek Rei<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.marekrei.com\/blog\/ml-and-nlp-publications-in-2019\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"ML and NLP Publications in 2019 - 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