{"id":1709,"date":"2021-02-11T13:48:20","date_gmt":"2021-02-11T13:48:20","guid":{"rendered":"http:\/\/www.marekrei.com\/blog\/?p=1709"},"modified":"2021-03-15T17:11:55","modified_gmt":"2021-03-15T17:11:55","slug":"ml-and-nlp-publications-in-2020","status":"publish","type":"post","link":"https:\/\/www.marekrei.com\/blog\/ml-and-nlp-publications-in-2020\/","title":{"rendered":"ML and NLP Publications in 2020"},"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\", \"Yang Li\", \"Hao Zhang\"]; \/\/ As far as I can tell, there are two or more researchers publishing under these names. So removing them 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}\nfunction removeZeroEntriesOfLineGraph(data){\n    \/\/ Removing entries with zeros\n    for (var i = 0; i < data.length; i++){\n        for (var j = 0; j < data[i].x.length; j++){\n            if(data[i].y[j] == 0.0 || data[i].y[j] == 0){\n                console.log(data[i].x[j]);\n                data[i].y.splice(j, 1);\n                data[i].x.splice(j, 1);\n                j--;\n            }\n        }\n    }\n}\n<\/script><\/p>\n<p>I ran my paper analysis pipeline once again in order to get statistics for 2020. It certainly was an unusual year. While ML and NLP conferences again had more publications than ever before, most of them needed to quickly adapt to a new remote format. Each conference took a slightly different approach as everyone was trying to figure out how to make this work. I heard especially positive comments about EMNLP 2020, regarding their smooth organisation and engaging technical solutions. Overall, I think the remote format has its pluses and minuses - while it certainly complicates networking and socialising, it also makes these conferences much more accessible to a wider range of audience. Hopefully the online participation options will be made available even after we are able to have in-person meetings again.<\/p>\n<p>This post includes the analysis of publications from the following conferences and journals: ACL, EMNLP, NAACL, EACL, AACL, COLING, TACL, CL, CoNLL, NeurIPS, ICML, ICLR, AAAI. All the information is crawled and processed automatically from the corresponding proceedings and directly from the pdf files. Some noise likely still remains, so the graphs are more indicative of general trends as opposed to specific paper counts. Big thanks to <a href=\"https:\/\/pfeiffer.ai\/\">Jonas Pfeiffer<\/a> (<a href=\"https:\/\/twitter.com\/PfeiffJo\">@PfeiffJo<\/a>) for adding the country annotations to all the new organisations, so that we can also run the country-level analysis below.<\/p>\n<p>These statistics are not meant to imply that the quantity of publications is an important measure of a researcher. Having one groundbreaking paper is always more meaningful than churning out many forgettable pieces. But it can be a good way of getting an overview of active research groups and hopefully it can inspire new researchers to publish their own work.<\/p>\n<h2>Venues<\/h2>\n<p>Let's start by looking at the overall number of papers published at different venues. Following the trends from the previous few years, most venues continued to break records in this regard. ML and AI conferences such as NeurIPS, AAAI and ICML had particularly large increases in paper numbers. ACL and EMNLP had more modest increments, but still more than ever before. CoNLL took a slight decrease in numbers, possibly due to narrowing their focus back towards linguistics as opposed to engineering. AACL was introduced as a brand new conference for the Asia-Pacific Chapter of ACL.<\/p>\n<div id=\"myplot_graph2020_all_source_year\"><\/div>\n<p><script>\nvar trace_graph2020_all_source_year_0 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [187, 328, 286, 318, 328, 302, 381, 660, 778, ],\n  text: \"ACL\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_source_year_1 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [139, 205, 226, 312, 264, 323, 549, 681, 751, ],\n  text: \"EMNLP\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_source_year_2 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [97, 140, 0, 186, 181, 0, 357, 451, 0, ],\n  text: \"NAACL\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_source_year_3 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [85, 0, 124, 0, 0, 239, 0, 0, 0, ],\n  text: \"EACL\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_source_year_4 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [0, 0, 0, 0, 0, 0, 0, 0, 92, ],\n  text: \"AACL\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_source_year_5 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [332, 0, 221, 0, 337, 0, 330, 0, 612, ],\n  text: \"COLING\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_source_year_6 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [0, 25, 20, 38, 31, 45, 56, 97, 53, ],\n  text: \"CoNLL\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_source_year_7 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [0, 35, 43, 42, 39, 37, 48, 45, 46, ],\n  text: \"TACL\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_source_year_8 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [36, 35, 35, 33, 31, 28, 34, 18, 17, ],\n  text: \"CL\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_source_year_9 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [368, 360, 411, 403, 569, 679, 1009, 1427, 1898, ],\n  text: \"NeurIPS\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_source_year_10 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [243, 283, 310, 270, 322, 434, 621, 773, 1085, ],\n  text: \"ICML\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_source_year_11 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [0, 23, 34, 42, 80, 198, 336, 502, 687, ],\n  text: \"ICLR\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_source_year_12 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [311, 202, 398, 538, 548, 639, 937, 1147, 1610, ],\n  text: \"AAAI\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar data_graph2020_all_source_year = [trace_graph2020_all_source_year_0, trace_graph2020_all_source_year_1, trace_graph2020_all_source_year_2, trace_graph2020_all_source_year_3, trace_graph2020_all_source_year_4, trace_graph2020_all_source_year_5, trace_graph2020_all_source_year_6, trace_graph2020_all_source_year_7, trace_graph2020_all_source_year_8, trace_graph2020_all_source_year_9, trace_graph2020_all_source_year_10, trace_graph2020_all_source_year_11, trace_graph2020_all_source_year_12];\nPlotly.newPlot('myplot_graph2020_all_source_year', data_graph2020_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>Looking at which organizations published most papers in 2020, it is clear to see that Google manages to dominate this space. Microsoft holds a respectable second position and CMU is the the top publishing university. MIT, Berkeley, DeepMind and Oxford are mostly publishing only at ML conferences. In contrast, Microsoft, Tencent, Uni. of CAS, Alibaba and Amazon have significant proportions of their publications at NLP conferences.<\/p>\n<div id=\"myplot_graph2020_last_org_source\" style=\"height:550px;\"><\/div>\n<p><script>\nvar trace_graph2020_last_org_source_0 = {  x: [\"Google\", \"Microsoft\", \"Carnegie Mellon University\", \"Stanford University\", \"Massachusetts Institute of Technology\", \"Tsinghua University\", \"Facebook\", \"Peking University\", \"UC Berkeley\", \"Chinese Academy of Sciences\", \"Tencent\", \"IBM\", \"DeepMind\", \"University of Chinese Academy of Sciences\", \"Alibaba AI Labs\", \"Amazon\", \"University of Oxford\", \"University of Cambridge\", \"University of Washington\", \"University of Illinois\", \"Princeton University\", \"New York University\", \"Cornell University\", \"Columbia University\", \"Nanjing University\", \"National University of Singapore\", \"Harvard University\", \"Zhejiang University\", \"Shanghai Jiao Tong University\", 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University of Hong Kong\", \"Georgia Institute of Technology\", \"University College London\", \"INRIA\", ],\n  y: [90, 27, 43, 36, 39, 18, 44, 15, 31, 4, 5, 11, 36, 2, 4, 6, 18, 15, 7, 14, 18, 15, 12, 9, 5, 12, 10, 4, 14, 8, 12, 8, 14, 5, 8, 12, 14, 4, 21, 11, 4, 5, 10, 10, 4, ],\n  name: 'ICLR',\n  type: 'bar'\n};\nvar trace_graph2020_last_org_source_2 = {  x: [\"Google\", \"Microsoft\", \"Carnegie Mellon University\", \"Stanford University\", \"Massachusetts Institute of Technology\", \"Tsinghua University\", \"Facebook\", \"Peking University\", \"UC Berkeley\", \"Chinese Academy of Sciences\", \"Tencent\", \"IBM\", \"DeepMind\", \"University of Chinese Academy of Sciences\", \"Alibaba AI Labs\", \"Amazon\", \"University of Oxford\", \"University of Cambridge\", \"University of Washington\", \"University of Illinois\", \"Princeton University\", \"New York University\", \"Cornell University\", \"Columbia University\", \"Nanjing University\", \"National University of Singapore\", 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\"University of Maryland\", \"University of Science and Technology of China\", \"The Chinese University of Hong Kong\", \"Georgia Institute of Technology\", \"University College London\", \"INRIA\", ],\n  y: [177, 107, 79, 99, 99, 65, 61, 35, 86, 17, 24, 40, 54, 8, 18, 21, 60, 36, 26, 42, 52, 49, 40, 46, 15, 22, 38, 11, 18, 37, 46, 22, 26, 5, 49, 32, 24, 14, 30, 18, 16, 18, 25, 34, 38, ],\n  name: 'NeurIPS',\n  type: 'bar'\n};\nvar trace_graph2020_last_org_source_4 = {  x: [\"Google\", \"Microsoft\", \"Carnegie Mellon University\", \"Stanford University\", \"Massachusetts Institute of Technology\", \"Tsinghua University\", \"Facebook\", \"Peking University\", \"UC Berkeley\", \"Chinese Academy of Sciences\", \"Tencent\", \"IBM\", \"DeepMind\", \"University of Chinese Academy of Sciences\", \"Alibaba AI Labs\", \"Amazon\", \"University of Oxford\", \"University of Cambridge\", \"University of Washington\", \"University of Illinois\", \"Princeton University\", \"New York University\", \"Cornell University\", \"Columbia University\", \"Nanjing University\", \"National University of Singapore\", \"Harvard University\", \"Zhejiang University\", \"Shanghai Jiao Tong University\", \"University of Texas at Austin\", \"Ecole Polytechnique Fe\u0301de\u0301rale de Lausanne\", \"Huawei\", \"University of California Los Angeles\", \"Allen Institute for Artificial Intelligence\", \"ETH Z\u00fcrich\", \"Korea Advanced Institute of Science and Technology\", \"University of Pennsylvania\", \"Johns Hopkins University\", \"University of Toronto\", \"University of Maryland\", \"University of Science and Technology of China\", \"The Chinese University of Hong Kong\", \"Georgia Institute of Technology\", \"University College London\", \"INRIA\", ],\n  y: [0, 1, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 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_graph2020_last_org_source_5 = {  x: [\"Google\", \"Microsoft\", \"Carnegie Mellon University\", \"Stanford University\", \"Massachusetts Institute of Technology\", \"Tsinghua University\", \"Facebook\", \"Peking University\", \"UC Berkeley\", \"Chinese Academy of Sciences\", \"Tencent\", \"IBM\", \"DeepMind\", \"University of Chinese Academy of Sciences\", \"Alibaba AI Labs\", \"Amazon\", \"University of Oxford\", \"University of Cambridge\", \"University of Washington\", \"University of Illinois\", \"Princeton University\", \"New York University\", \"Cornell University\", \"Columbia University\", \"Nanjing University\", \"National University of Singapore\", \"Harvard University\", \"Zhejiang University\", \"Shanghai Jiao Tong University\", \"University of Texas at Austin\", \"Ecole Polytechnique Fe\u0301de\u0301rale de Lausanne\", \"Huawei\", \"University of California Los Angeles\", \"Allen Institute for Artificial Intelligence\", \"ETH Z\u00fcrich\", \"Korea Advanced Institute of Science and Technology\", \"University of Pennsylvania\", \"Johns Hopkins University\", \"University of Toronto\", \"University of Maryland\", \"University of Science and Technology of China\", \"The Chinese University of Hong Kong\", \"Georgia Institute of Technology\", \"University College London\", \"INRIA\", ],\n  y: [5, 1, 4, 1, 0, 2, 2, 1, 0, 0, 1, 0, 2, 0, 0, 1, 1, 2, 1, 0, 1, 2, 1, 2, 0, 0, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0, 4, 0, 0, 0, 0, 0, 1, 0, ],\n  name: 'TACL',\n  type: 'bar'\n};\nvar trace_graph2020_last_org_source_6 = {  x: [\"Google\", \"Microsoft\", \"Carnegie Mellon University\", \"Stanford University\", \"Massachusetts Institute of Technology\", \"Tsinghua University\", \"Facebook\", \"Peking University\", \"UC Berkeley\", \"Chinese Academy of Sciences\", \"Tencent\", \"IBM\", \"DeepMind\", \"University of Chinese Academy of Sciences\", \"Alibaba AI Labs\", \"Amazon\", \"University of Oxford\", \"University of Cambridge\", \"University of Washington\", \"University of Illinois\", \"Princeton University\", 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'AACL',\n  type: 'bar'\n};\nvar trace_graph2020_last_org_source_9 = {  x: [\"Google\", \"Microsoft\", \"Carnegie Mellon University\", \"Stanford University\", \"Massachusetts Institute of Technology\", \"Tsinghua University\", \"Facebook\", \"Peking University\", \"UC Berkeley\", \"Chinese Academy of Sciences\", \"Tencent\", \"IBM\", \"DeepMind\", \"University of Chinese Academy of Sciences\", \"Alibaba AI Labs\", \"Amazon\", \"University of Oxford\", \"University of Cambridge\", \"University of Washington\", \"University of Illinois\", \"Princeton University\", \"New York University\", \"Cornell University\", \"Columbia University\", \"Nanjing University\", \"National University of Singapore\", \"Harvard University\", \"Zhejiang University\", \"Shanghai Jiao Tong University\", \"University of Texas at Austin\", \"Ecole Polytechnique Fe\u0301de\u0301rale de Lausanne\", \"Huawei\", \"University of California Los Angeles\", \"Allen Institute for Artificial Intelligence\", \"ETH 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\"University of Cambridge\", \"University of Washington\", \"University of Illinois\", \"Princeton University\", \"New York University\", \"Cornell University\", \"Columbia University\", \"Nanjing University\", \"National University of Singapore\", \"Harvard University\", \"Zhejiang University\", \"Shanghai Jiao Tong University\", \"University of Texas at Austin\", \"Ecole Polytechnique Fe\u0301de\u0301rale de Lausanne\", \"Huawei\", \"University of California Los Angeles\", \"Allen Institute for Artificial Intelligence\", \"ETH Z\u00fcrich\", \"Korea Advanced Institute of Science and Technology\", \"University of Pennsylvania\", \"Johns Hopkins University\", \"University of Toronto\", \"University of Maryland\", \"University of Science and Technology of China\", \"The Chinese University of Hong Kong\", \"Georgia Institute of Technology\", \"University College London\", \"INRIA\", ],\n  y: [31, 57, 40, 19, 13, 23, 33, 25, 8, 22, 33, 18, 8, 16, 24, 24, 4, 19, 25, 10, 1, 7, 13, 9, 5, 7, 4, 12, 8, 6, 2, 4, 10, 32, 3, 4, 10, 23, 0, 10, 3, 8, 2, 7, 5, ],\n  name: 'ACL',\n  type: 'bar'\n};\nvar data_graph2020_last_org_source = [trace_graph2020_last_org_source_0, trace_graph2020_last_org_source_1, trace_graph2020_last_org_source_2, trace_graph2020_last_org_source_3, trace_graph2020_last_org_source_4, trace_graph2020_last_org_source_5, trace_graph2020_last_org_source_6, trace_graph2020_last_org_source_7, trace_graph2020_last_org_source_8, trace_graph2020_last_org_source_9, trace_graph2020_last_org_source_10];\nfixBarGraph(data_graph2020_last_org_source);\nscaleBarGraph(data_graph2020_last_org_source, 32);\nPlotly.newPlot('myplot_graph2020_last_org_source', data_graph2020_last_org_source, {barmode: 'stack', margin: { l: 30, r: 0, b: 270, t: 20, pad: 4 }});\n<\/script><\/p>\n<p>Looking at the statistics for the whole 2012-2020 period, the top three positions are nailbitingly close: Microsoft still leads with 1580, then Google with 1570 and CMU with 1537.<\/p>\n<div id=\"myplot_graph2020_all_org_source\" style=\"height:550px;\"><\/div>\n<p><script>\nvar trace_graph2020_all_org_source_0 = {  x: [\"Microsoft\", \"Google\", \"Carnegie Mellon University\", \"Stanford University\", \"Massachusetts Institute of Technology\", \"UC Berkeley\", \"IBM\", \"Tsinghua University\", \"Peking University\", \"Facebook\", \"Chinese Academy of Sciences\", \"University of Washington\", \"University of Cambridge\", \"DeepMind\", \"University of Oxford\", \"Tencent\", \"University of Edinburgh\", \"University of Illinois\", \"Columbia University\", \"University of Texas at Austin\", \"Cornell University\", \"Johns Hopkins University\", \"Princeton University\", \"University of Chinese Academy of Sciences\", \"University of Toronto\", \"Harvard University\", \"University of Pennsylvania\", \"Amazon\", \"INRIA\", \"New York University\", \"University of Southern California\", \"Nanjing University\", \"Georgia Institute of Technology\", \"National University of 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trace_graph2020_all_org_year_19, trace_graph2020_all_org_year_20, trace_graph2020_all_org_year_21, trace_graph2020_all_org_year_22, trace_graph2020_all_org_year_23, trace_graph2020_all_org_year_24, trace_graph2020_all_org_year_25, trace_graph2020_all_org_year_26, trace_graph2020_all_org_year_27, trace_graph2020_all_org_year_28, trace_graph2020_all_org_year_29, trace_graph2020_all_org_year_30, trace_graph2020_all_org_year_31, trace_graph2020_all_org_year_32, trace_graph2020_all_org_year_33, trace_graph2020_all_org_year_34, trace_graph2020_all_org_year_35, trace_graph2020_all_org_year_36, trace_graph2020_all_org_year_37, trace_graph2020_all_org_year_38, trace_graph2020_all_org_year_39, trace_graph2020_all_org_year_40, trace_graph2020_all_org_year_41, trace_graph2020_all_org_year_42, trace_graph2020_all_org_year_43, trace_graph2020_all_org_year_44];\nfixLineGraph(data_graph2020_all_org_year);\nscaleLineGraph(data_graph2020_all_org_year, 15);\nPlotly.newPlot('myplot_graph2020_all_org_year', data_graph2020_all_org_year, {margin: { l: 30, r: 0, b: 20, t: 0, pad: 4 }, showlegend: false, showticklabels: true, xaxis:{autotick: false}});\n<\/script><\/p>\n<h2>Authors<\/h2>\n<p>Next, let's look at the researchers who published the most papers in 2020. <a href=\"http:\/\/www.phontron.com\/\">Graham Neubig<\/a> from CMU ranks first in this aspect, with 31 publications. Others with impressive numbers of papers include <a href=\"https:\/\/frcchang.github.io\/\">Yue Zhang<\/a> (Westlake University), <a href=\"https:\/\/people.eecs.berkeley.edu\/~svlevine\/\">Sergey Levine<\/a> (UC Berkeley), <a href=\"http:\/\/encs.hit.edu.cn\/2016\/0310\/c5511a145533\/page.htm\">Ting Liu<\/a> (Harbin Institute of Technology), <a href=\"http:\/\/nlp.csai.tsinghua.edu.cn\/~lzy\/\">Zhiyuan Liu<\/a> (Tsinghua University) and <a href=\"https:\/\/scholar.google.co.jp\/citations?user=a0w5c0gAAAAJ&#038;hl=en\">Ming Zhou<\/a> (Microsoft Research Asia).<\/p>\n<div id=\"myplot_graph2020_last_author_source\"><\/div>\n<p><script>\nvar trace_graph2020_last_author_source_0 = {  x: [\"Graham Neubig\", \"Yue Zhang\", \"Sergey Levine\", \"Ting Liu\", \"Zhiyuan Liu\", \"Ming Zhou\", \"Wei Wang\", \"Wei Zhang\", \"Jie Zhou\", \"Lawrence Carin\", \"Sung Ju Hwang\", \"Stefano Ermon\", \"Zhangyang Wang\", \"Rui Wang\", \"Qi Zhang\", \"Cho-Jui Hsieh\", \"Furu Wei\", \"Xuanjing Huang\", 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Manning\", \"Le Song\", \"Mohit Bansal\", \"Stefano Ermon\", \"Eduard Hovy\", \"Benjamin Van Durme\", \"Qun Liu\", \"Andreas Krause\", \"Min Zhang\", \"Furu Wei\", \"Yejin Choi\", \"Tong Zhang\", \"Jianfeng Gao\", \"Heng Ji\", \"Masashi Sugiyama\", \"Kyunghyun Cho\", \"Ryan Cotterell\", \"Guodong Zhou\", \"Tie-Yan Liu\", \"Trevor Cohn\", \"Pushpak Bhattacharyya\", \"Dan Klein\", \"Richard Socher\", ],\n  y: [51, 8, 38, 29, 29, 38, 0, 34, 10, 28, 20, 17, 24, 28, 22, 26, 22, 4, 30, 20, 0, 21, 26, 1, 20, 0, 23, 28, 26, 0, 19, 20, 21, 4, 17, 31, 0, 10, 20, 17, 6, 26, 20, 26, 12, ],\n  name: 'ACL',\n  type: 'bar'\n};\nvar data_graph2020_all_author_source = [trace_graph2020_all_author_source_0, trace_graph2020_all_author_source_1, trace_graph2020_all_author_source_2, trace_graph2020_all_author_source_3, trace_graph2020_all_author_source_4, trace_graph2020_all_author_source_5, trace_graph2020_all_author_source_6, trace_graph2020_all_author_source_7, trace_graph2020_all_author_source_8, trace_graph2020_all_author_source_9, trace_graph2020_all_author_source_10, trace_graph2020_all_author_source_11, trace_graph2020_all_author_source_12];\nfixBarGraph(data_graph2020_all_author_source);\nscaleBarGraph(data_graph2020_all_author_source, 32);\nPlotly.newPlot('myplot_graph2020_all_author_source', data_graph2020_all_author_source, {barmode: 'stack', margin: { l: 30, r: 0, b: 150, t: 20, pad: 4 }});\n<\/script><\/p>\n<p>The breakdown over the years gives an overview of when each researcher has published most. Yue Zhang and Ting Liu have both improved their overall ranking, having had a particularly successful last year in terms of publishing. While Sergey Levine still holds the overall record of most papers per year, Graham Neubig managed to publish the most in 2020.<\/p>\n<div id=\"myplot_graph2020_all_author_year\"><\/div>\n<p><script>\nvar trace_graph2020_all_author_year_0 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [13, 9, 14, 9, 12, 11, 21, 14, 28, ],\n  text: \"Ming Zhou\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_author_year_1 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [3, 9, 16, 8, 15, 17, 16, 29, 15, ],\n  text: \"Yoshua Bengio\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_author_year_2 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [3, 4, 13, 10, 23, 14, 13, 18, 30, ],\n  text: \"Yue Zhang\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_author_year_3 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [11, 9, 6, 18, 15, 14, 19, 17, 18, ],\n  text: \"Yang Liu\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_author_year_4 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [10, 5, 15, 8, 13, 5, 17, 17, 29, ],\n  text: \"Ting Liu\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_author_year_5 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [2, 1, 6, 5, 9, 12, 23, 30, 31, ],\n  text: \"Graham Neubig\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_author_year_6 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [1, 2, 2, 1, 10, 11, 22, 33, 30, ],\n  text: \"Sergey Levine\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_author_year_7 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [9, 10, 14, 15, 10, 7, 13, 17, 14, ],\n  text: \"Noah A. Smith\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_author_year_8 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [7, 5, 8, 12, 9, 16, 14, 17, 21, ],\n  text: \"Lawrence Carin\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_author_year_9 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [3, 10, 14, 22, 25, 12, 11, 8, 3, ],\n  text: \"Chris Dyer\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_author_year_10 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [9, 6, 6, 5, 12, 12, 18, 16, 17, ],\n  text: \"Iryna Gurevych\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_author_year_11 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [8, 7, 4, 8, 12, 8, 18, 16, 17, ],\n  text: \"Dan Roth\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_author_year_12 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [5, 4, 8, 12, 15, 12, 12, 12, 17, ],\n  text: \"Hinrich Sch\u00fctze\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_author_year_13 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [4, 0, 2, 7, 5, 4, 18, 25, 28, ],\n  text: \"Zhiyuan Liu\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_author_year_14 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [1, 5, 7, 11, 9, 13, 17, 10, 18, ],\n  text: \"Percy Liang\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_author_year_15 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [6, 2, 3, 9, 8, 9, 20, 15, 18, ],\n  text: \"Maosong Sun\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_author_year_16 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [8, 4, 6, 6, 8, 10, 16, 17, 11, ],\n  text: \"Mirella Lapata\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_author_year_17 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [3, 4, 10, 4, 12, 5, 16, 12, 19, ],\n  text: \"Jun Zhu\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_author_year_18 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [3, 9, 4, 6, 7, 6, 16, 16, 17, ],\n  text: \"Luke Zettlemoyer\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_author_year_19 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [2, 6, 6, 12, 12, 11, 13, 14, 8, ],\n  text: \"Anders S\u00f8gaard\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_author_year_20 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [2, 0, 1, 4, 10, 13, 17, 19, 16, ],\n  text: \"Pieter Abbeel\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_author_year_21 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [3, 3, 3, 8, 13, 5, 12, 13, 19, ],\n  text: \"Xuanjing Huang\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_author_year_22 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [7, 12, 14, 11, 8, 6, 7, 6, 7, ],\n  text: \"Christopher D. Manning\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_author_year_23 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [1, 5, 8, 5, 5, 11, 15, 12, 16, ],\n  text: \"Le Song\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_author_year_24 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [2, 1, 3, 4, 10, 6, 18, 16, 18, ],\n  text: \"Mohit Bansal\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_author_year_25 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [1, 2, 2, 3, 11, 8, 15, 15, 20, ],\n  text: \"Stefano Ermon\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_author_year_26 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [1, 5, 12, 5, 7, 9, 10, 16, 12, ],\n  text: \"Eduard Hovy\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_author_year_27 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [4, 9, 7, 10, 4, 11, 9, 13, 9, ],\n  text: \"Benjamin Van Durme\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_author_year_28 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [9, 8, 9, 4, 12, 11, 7, 5, 10, ],\n  text: \"Qun Liu\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_author_year_29 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [2, 4, 5, 8, 10, 13, 9, 13, 10, ],\n  text: \"Andreas Krause\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_author_year_30 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [7, 4, 6, 4, 8, 4, 10, 12, 18, ],\n  text: \"Min Zhang\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_author_year_31 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [7, 2, 5, 6, 4, 5, 11, 12, 19, ],\n  text: \"Furu Wei\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_author_year_32 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [4, 5, 3, 4, 6, 8, 12, 14, 15, ],\n  text: \"Yejin Choi\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_author_year_33 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [2, 3, 4, 6, 4, 7, 21, 9, 15, ],\n  text: \"Tong Zhang\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_author_year_34 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [2, 2, 6, 6, 7, 4, 10, 18, 15, ],\n  text: \"Jianfeng Gao\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_author_year_35 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [2, 4, 10, 13, 9, 6, 10, 9, 7, ],\n  text: \"Heng Ji\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_author_year_36 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [5, 4, 5, 2, 2, 7, 11, 16, 18, ],\n  text: \"Masashi Sugiyama\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_author_year_37 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [0, 1, 5, 6, 11, 7, 14, 11, 15, ],\n  text: \"Kyunghyun Cho\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_author_year_38 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [0, 0, 1, 6, 6, 10, 9, 21, 17, ],\n  text: \"Ryan Cotterell\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_author_year_39 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [13, 4, 7, 6, 7, 1, 12, 10, 9, ],\n  text: \"Guodong Zhou\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_author_year_40 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [0, 2, 6, 1, 5, 10, 13, 23, 9, ],\n  text: \"Tie-Yan Liu\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_author_year_41 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [2, 6, 5, 12, 13, 11, 11, 7, 2, ],\n  text: \"Trevor Cohn\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_author_year_42 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [5, 6, 1, 7, 9, 6, 13, 9, 12, ],\n  text: \"Pushpak Bhattacharyya\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_author_year_43 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [9, 8, 8, 8, 4, 10, 6, 6, 9, ],\n  text: \"Dan Klein\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_author_year_44 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [3, 6, 4, 1, 2, 6, 10, 17, 18, ],\n  text: \"Richard Socher\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar data_graph2020_all_author_year = [trace_graph2020_all_author_year_0, trace_graph2020_all_author_year_1, trace_graph2020_all_author_year_2, trace_graph2020_all_author_year_3, trace_graph2020_all_author_year_4, trace_graph2020_all_author_year_5, trace_graph2020_all_author_year_6, trace_graph2020_all_author_year_7, trace_graph2020_all_author_year_8, trace_graph2020_all_author_year_9, trace_graph2020_all_author_year_10, trace_graph2020_all_author_year_11, trace_graph2020_all_author_year_12, trace_graph2020_all_author_year_13, trace_graph2020_all_author_year_14, trace_graph2020_all_author_year_15, trace_graph2020_all_author_year_16, trace_graph2020_all_author_year_17, trace_graph2020_all_author_year_18, trace_graph2020_all_author_year_19, trace_graph2020_all_author_year_20, trace_graph2020_all_author_year_21, trace_graph2020_all_author_year_22, trace_graph2020_all_author_year_23, trace_graph2020_all_author_year_24, trace_graph2020_all_author_year_25, trace_graph2020_all_author_year_26, trace_graph2020_all_author_year_27, trace_graph2020_all_author_year_28, trace_graph2020_all_author_year_29, trace_graph2020_all_author_year_30, trace_graph2020_all_author_year_31, trace_graph2020_all_author_year_32, trace_graph2020_all_author_year_33, trace_graph2020_all_author_year_34, trace_graph2020_all_author_year_35, trace_graph2020_all_author_year_36, trace_graph2020_all_author_year_37, trace_graph2020_all_author_year_38, trace_graph2020_all_author_year_39, trace_graph2020_all_author_year_40, trace_graph2020_all_author_year_41, trace_graph2020_all_author_year_42, trace_graph2020_all_author_year_43, trace_graph2020_all_author_year_44];\nfixLineGraph(data_graph2020_all_author_year);\nscaleLineGraph(data_graph2020_all_author_year, 16);\nPlotly.newPlot('myplot_graph2020_all_author_year', data_graph2020_all_author_year, {margin: { l: 30, r: 0, b: 20, t: 0, pad: 4 }, showlegend: false, showticklabels: true, xaxis:{autotick: false}});\n<\/script><\/p>\n<h2>First Authors<\/h2>\n<p>The most published researchers are generally supervisors for many students\/post-docs performing the practical experiments. In contrast, first authors are usually those who do the legwork, implementing the actual code and writing much of the paper. <\/p>\n<p>Normally, I would make a chart showing the most publishing first authors from the last year. However, this time all the top ranks were cases where two or more people were publishing under the same name. I don't currently have any technology that can automatically identify and disambiguate these authors (future research project perhaps?) so I'm skipping this graph and jumping straight to the overall statistics between 2012-2020. <a href=\"http:\/\/people.csail.mit.edu\/zeyuan\/\">Zeyuan Allen-Zhu<\/a> (MSR AI), <a href=\"https:\/\/nlp.stanford.edu\/~bdlijiwei\/\">Jiwei Li<\/a> (Shannon.AI) and <a href=\"https:\/\/sites.google.com\/site\/ivanvulic\/\">Ivan Vuli\u0107<\/a> (Cambridge\/PolyAI) have the most impressive publication records as first authors, with Zeyuan publishing in the ML area, Jiwei and Ivan publishing mostly in NLP.<\/p>\n<div id=\"myplot_graph2020_all_firstauthor_source\"><\/div>\n<p><script>\nvar trace_graph2020_all_firstauthor_source_0 = {  x: [\"Yang Liu\", \"Zeyuan Allen-Zhu\", \"Jiwei Li\", \"Ivan Vuli\u0107\", \"Sanjeev Arora\", \"Yi Tay\", \"Ryan Cotterell\", \"Young-Bum Kim\", \"Corinna Cortes\", \"Wenpeng Yin\", \"Hao Wang\", \"Hao Zhang\", \"Elad Hazan\", \"Di 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Yin\", \"Hao Wang\", \"Hao Zhang\", \"Elad Hazan\", \"Di Wang\", \"Jacob Andreas\", \"Nathan Kallus\", \"Haris Aziz\", \"Chen Chen\", \"Gabriele Farina\", \"Meishan Zhang\", \"Mikel Artetxe\", \"Fan Yang\", \"Ilias Diakonikolas\", \"Yang Li\", \"Omer Levy\", \"Lijun Zhang\", \"Yang Yang\", \"Yining Wang\", \"Hao Peng\", \"Yuan Zhang\", \"Douwe Kiela\", \"Shafiq Joty\", \"Yi Yang\", \"Ellie Pavlick\", \"Min Xiao\", \"Qiang Liu\", \"Qi Zhang\", \"Anders S\u00f8gaard\", \"Han Zhao\", \"Jiatao Gu\", \"Xiao Zhang\", \"Angeliki Lazaridou\", \"Annie Louis\", \"Dirk Hovy\", \"Yuchen Zhang\", ],\n  y: [3, 0, 6, 6, 0, 5, 3, 9, 1, 3, 0, 3, 0, 1, 4, 0, 0, 2, 0, 3, 7, 0, 0, 2, 4, 0, 0, 1, 4, 4, 3, 3, 2, 7, 1, 0, 0, 4, 0, 3, 2, 6, 1, 7, 0, ],\n  name: 'ACL',\n  type: 'bar'\n};\nvar data_graph2020_all_firstauthor_source = [trace_graph2020_all_firstauthor_source_0, trace_graph2020_all_firstauthor_source_1, trace_graph2020_all_firstauthor_source_2, trace_graph2020_all_firstauthor_source_3, trace_graph2020_all_firstauthor_source_4, trace_graph2020_all_firstauthor_source_5, trace_graph2020_all_firstauthor_source_6, trace_graph2020_all_firstauthor_source_7, trace_graph2020_all_firstauthor_source_8, trace_graph2020_all_firstauthor_source_9, trace_graph2020_all_firstauthor_source_10, trace_graph2020_all_firstauthor_source_11, trace_graph2020_all_firstauthor_source_12];\nfixBarGraph(data_graph2020_all_firstauthor_source);\nscaleBarGraph(data_graph2020_all_firstauthor_source, 31);\nPlotly.newPlot('myplot_graph2020_all_firstauthor_source', data_graph2020_all_firstauthor_source, {barmode: 'stack', margin: { l: 30, r: 0, b: 130, t: 20, pad: 4 }});\n<\/script><\/p>\n<h2>Countries<\/h2>\n<p>Separating the statistics by country highlights just how much the United States publishes. China is giving a strong effort as well, with the UK being third.<\/p>\n<div id=\"myplot_graph2020_last_country_source\"><\/div>\n<p><script>\nvar trace_graph2020_last_country_source_0 = {  x: [\"USA\", \"China\", \"UK\", \"Germany\", \"Canada\", \"Australia\", \"Singapore\", \"South Korea\", \"Japan\", \"Switzerland\", \"France\", \"Israel\", \"India\", \"Italy\", \"Netherlands\", \"Denmark\", \"Taiwan\", \"Sweden\", \"Finland\", \"Spain\", \"Russia\", \"Austria\", \"Hong Kong\", \"Poland\", \"Belgium\", \"Saudi Arabia\", \"Ireland\", \"UAE\", \"Macau\", \"Qatar\", \"Brazil\", \"Vietnam\", \"Czech Republic\", \"Norway\", \"Greece\", \"Iran\", \"Portugal\", \"Romania\", \"Thailand\", \"New Zealand\", \"Luxembourg\", \"Turkey\", \"Bulgaria\", \"South Africa\", \"Hungary\", ],\n  y: [625, 763, 110, 44, 47, 100, 67, 55, 45, 13, 18, 24, 28, 36, 8, 9, 14, 7, 18, 6, 5, 8, 11, 8, 6, 5, 1, 9, 6, 1, 1, 3, 5, 2, 3, 2, 2, 2, 1, 3, 1, 1, 0, 0, 1, ],\n  name: 'AAAI',\n  type: 'bar'\n};\nvar trace_graph2020_last_country_source_1 = {  x: [\"USA\", \"China\", \"UK\", \"Germany\", \"Canada\", \"Australia\", \"Singapore\", \"South Korea\", \"Japan\", \"Switzerland\", \"France\", \"Israel\", \"India\", \"Italy\", \"Netherlands\", \"Denmark\", \"Taiwan\", \"Sweden\", \"Finland\", \"Spain\", \"Russia\", \"Austria\", \"Hong Kong\", \"Poland\", \"Belgium\", \"Saudi Arabia\", \"Ireland\", \"UAE\", \"Macau\", \"Qatar\", \"Brazil\", \"Vietnam\", \"Czech Republic\", \"Norway\", \"Greece\", \"Iran\", \"Portugal\", \"Romania\", \"Thailand\", \"New Zealand\", \"Luxembourg\", \"Turkey\", \"Bulgaria\", \"South Africa\", \"Hungary\", ],\n  y: [435, 99, 79, 42, 37, 16, 22, 26, 8, 26, 11, 15, 7, 4, 10, 2, 3, 4, 1, 1, 3, 2, 0, 3, 0, 4, 1, 0, 1, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 1, 1, 0, 0, 1, 1, ],\n  name: 'ICLR',\n  type: 'bar'\n};\nvar trace_graph2020_last_country_source_2 = {  x: [\"USA\", \"China\", \"UK\", \"Germany\", \"Canada\", \"Australia\", \"Singapore\", \"South Korea\", \"Japan\", \"Switzerland\", \"France\", \"Israel\", \"India\", \"Italy\", \"Netherlands\", \"Denmark\", \"Taiwan\", \"Sweden\", \"Finland\", \"Spain\", \"Russia\", \"Austria\", \"Hong Kong\", \"Poland\", \"Belgium\", \"Saudi Arabia\", \"Ireland\", \"UAE\", \"Macau\", \"Qatar\", \"Brazil\", \"Vietnam\", \"Czech Republic\", \"Norway\", \"Greece\", \"Iran\", \"Portugal\", \"Romania\", \"Thailand\", \"New Zealand\", \"Luxembourg\", \"Turkey\", \"Bulgaria\", \"South Africa\", \"Hungary\", ],\n  y: [636, 105, 109, 43, 67, 18, 25, 26, 21, 33, 38, 34, 12, 10, 9, 6, 3, 10, 3, 2, 12, 6, 6, 4, 4, 3, 0, 2, 2, 0, 1, 2, 0, 1, 2, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, ],\n  name: 'ICML',\n  type: 'bar'\n};\nvar trace_graph2020_last_country_source_3 = {  x: [\"USA\", \"China\", \"UK\", \"Germany\", \"Canada\", \"Australia\", \"Singapore\", \"South Korea\", \"Japan\", \"Switzerland\", \"France\", \"Israel\", \"India\", \"Italy\", \"Netherlands\", \"Denmark\", \"Taiwan\", \"Sweden\", \"Finland\", \"Spain\", \"Russia\", 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Republic\", \"Norway\", \"Greece\", \"Iran\", \"Portugal\", \"Romania\", \"Thailand\", \"New Zealand\", \"Luxembourg\", \"Turkey\", \"Bulgaria\", \"South Africa\", \"Hungary\", ],\n  y: [8, 1, 1, 2, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 2, 2, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ],\n  name: 'CL',\n  type: 'bar'\n};\nvar trace_graph2020_last_country_source_5 = {  x: [\"USA\", \"China\", \"UK\", \"Germany\", \"Canada\", \"Australia\", \"Singapore\", \"South Korea\", \"Japan\", \"Switzerland\", \"France\", \"Israel\", \"India\", \"Italy\", \"Netherlands\", \"Denmark\", \"Taiwan\", \"Sweden\", \"Finland\", \"Spain\", \"Russia\", \"Austria\", \"Hong Kong\", \"Poland\", \"Belgium\", \"Saudi Arabia\", \"Ireland\", \"UAE\", \"Macau\", \"Qatar\", \"Brazil\", \"Vietnam\", \"Czech Republic\", \"Norway\", \"Greece\", \"Iran\", \"Portugal\", \"Romania\", \"Thailand\", \"New Zealand\", \"Luxembourg\", \"Turkey\", \"Bulgaria\", \"South Africa\", \"Hungary\", 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type: 'bar'\n};\nvar trace_graph2020_last_country_source_7 = {  x: [\"USA\", \"China\", \"UK\", \"Germany\", \"Canada\", \"Australia\", \"Singapore\", \"South Korea\", \"Japan\", \"Switzerland\", \"France\", \"Israel\", \"India\", \"Italy\", \"Netherlands\", \"Denmark\", \"Taiwan\", \"Sweden\", \"Finland\", \"Spain\", \"Russia\", \"Austria\", \"Hong Kong\", \"Poland\", \"Belgium\", \"Saudi Arabia\", \"Ireland\", \"UAE\", \"Macau\", \"Qatar\", \"Brazil\", \"Vietnam\", \"Czech Republic\", \"Norway\", \"Greece\", \"Iran\", \"Portugal\", \"Romania\", \"Thailand\", \"New Zealand\", \"Luxembourg\", \"Turkey\", \"Bulgaria\", \"South Africa\", \"Hungary\", ],\n  y: [164, 185, 44, 61, 24, 25, 15, 17, 44, 5, 19, 4, 20, 12, 15, 5, 5, 5, 2, 10, 6, 2, 1, 3, 3, 1, 8, 3, 1, 2, 2, 2, 0, 0, 0, 1, 1, 4, 2, 0, 1, 0, 0, 0, 0, ],\n  name: 'COLING',\n  type: 'bar'\n};\nvar trace_graph2020_last_country_source_8 = {  x: [\"USA\", \"China\", \"UK\", \"Germany\", \"Canada\", \"Australia\", \"Singapore\", \"South Korea\", \"Japan\", \"Switzerland\", \"France\", \"Israel\", \"India\", \"Italy\", \"Netherlands\", \"Denmark\", \"Taiwan\", \"Sweden\", \"Finland\", \"Spain\", \"Russia\", \"Austria\", \"Hong Kong\", \"Poland\", \"Belgium\", \"Saudi Arabia\", \"Ireland\", \"UAE\", \"Macau\", \"Qatar\", \"Brazil\", \"Vietnam\", \"Czech Republic\", \"Norway\", \"Greece\", \"Iran\", \"Portugal\", \"Romania\", \"Thailand\", \"New Zealand\", \"Luxembourg\", \"Turkey\", \"Bulgaria\", \"South Africa\", \"Hungary\", ],\n  y: [41, 31, 8, 5, 4, 2, 1, 3, 6, 1, 1, 1, 5, 1, 2, 0, 4, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ],\n  name: 'AACL',\n  type: 'bar'\n};\nvar trace_graph2020_last_country_source_9 = {  x: [\"USA\", \"China\", \"UK\", \"Germany\", \"Canada\", \"Australia\", \"Singapore\", \"South Korea\", \"Japan\", \"Switzerland\", \"France\", \"Israel\", \"India\", \"Italy\", \"Netherlands\", \"Denmark\", \"Taiwan\", \"Sweden\", \"Finland\", \"Spain\", \"Russia\", \"Austria\", \"Hong Kong\", \"Poland\", \"Belgium\", \"Saudi Arabia\", \"Ireland\", \"UAE\", \"Macau\", \"Qatar\", \"Brazil\", \"Vietnam\", \"Czech Republic\", \"Norway\", \"Greece\", \"Iran\", \"Portugal\", \"Romania\", \"Thailand\", \"New Zealand\", \"Luxembourg\", \"Turkey\", \"Bulgaria\", \"South Africa\", \"Hungary\", ],\n  y: [424, 197, 65, 34, 27, 9, 29, 16, 15, 15, 7, 9, 14, 6, 15, 11, 7, 1, 2, 5, 1, 1, 1, 0, 0, 0, 3, 0, 1, 5, 1, 3, 0, 2, 1, 1, 2, 0, 1, 0, 0, 1, 1, 0, 1, ],\n  name: 'EMNLP',\n  type: 'bar'\n};\nvar trace_graph2020_last_country_source_10 = {  x: [\"USA\", \"China\", \"UK\", \"Germany\", \"Canada\", \"Australia\", \"Singapore\", \"South Korea\", \"Japan\", \"Switzerland\", \"France\", \"Israel\", \"India\", \"Italy\", \"Netherlands\", \"Denmark\", \"Taiwan\", \"Sweden\", \"Finland\", \"Spain\", \"Russia\", \"Austria\", \"Hong Kong\", \"Poland\", \"Belgium\", \"Saudi Arabia\", \"Ireland\", \"UAE\", \"Macau\", \"Qatar\", \"Brazil\", \"Vietnam\", \"Czech Republic\", \"Norway\", \"Greece\", \"Iran\", \"Portugal\", \"Romania\", \"Thailand\", \"New Zealand\", \"Luxembourg\", \"Turkey\", \"Bulgaria\", \"South Africa\", \"Hungary\", ],\n  y: [404, 232, 78, 46, 21, 14, 22, 10, 27, 13, 7, 19, 12, 9, 12, 9, 6, 2, 0, 10, 1, 0, 2, 1, 1, 0, 6, 1, 3, 4, 3, 1, 2, 3, 1, 2, 0, 0, 0, 1, 0, 0, 3, 0, 0, ],\n  name: 'ACL',\n  type: 'bar'\n};\nvar data_graph2020_last_country_source = [trace_graph2020_last_country_source_0, trace_graph2020_last_country_source_1, trace_graph2020_last_country_source_2, trace_graph2020_last_country_source_3, trace_graph2020_last_country_source_4, trace_graph2020_last_country_source_5, trace_graph2020_last_country_source_6, trace_graph2020_last_country_source_7, trace_graph2020_last_country_source_8, trace_graph2020_last_country_source_9, trace_graph2020_last_country_source_10];\nfixBarGraph(data_graph2020_last_country_source);\nscaleBarGraph(data_graph2020_last_country_source, 32);\nPlotly.newPlot('myplot_graph2020_last_country_source', data_graph2020_last_country_source, {barmode: 'stack', margin: { l: 40, r: 0, b: 100, t: 20, pad: 4 }});\n<\/script><\/p>\n<p>While China is definitely publishing more and more every year, it seems the US still manages to increase the lead. Overall, there is a general trend for more papers and most countries continue to increase their scientific output. <\/p>\n<div id=\"myplot_graph2020_all_country_year\"><\/div>\n<p><script>\nvar trace_graph2020_all_country_year_0 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [861, 908, 1085, 1137, 1401, 1553, 2464, 3119, 3961, ],\n  text: \"USA\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_country_year_1 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [193, 165, 270, 307, 448, 480, 975, 1354, 1918, ],\n  text: \"China\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_country_year_2 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [126, 133, 201, 213, 298, 315, 478, 571, 725, ],\n  text: \"UK\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_country_year_3 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [133, 96, 116, 120, 155, 148, 220, 280, 366, ],\n  text: \"Germany\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_country_year_4 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [100, 80, 102, 110, 121, 121, 191, 264, 324, ],\n  text: \"Canada\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_country_year_5 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [80, 60, 77, 87, 108, 106, 141, 171, 211, ],\n  text: \"Japan\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_country_year_6 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [62, 35, 74, 80, 87, 101, 117, 143, 240, ],\n  text: \"Australia\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_country_year_7 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [63, 47, 58, 64, 84, 77, 129, 146, 220, ],\n  text: \"Singapore\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_country_year_8 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [63, 52, 62, 70, 75, 87, 116, 148, 195, ],\n  text: \"France\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_country_year_9 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [57, 64, 65, 73, 76, 83, 120, 146, 165, ],\n  text: \"Israel\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_country_year_10 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [38, 36, 40, 41, 53, 81, 126, 140, 208, ],\n  text: \"Switzerland\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_country_year_11 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [16, 10, 17, 28, 33, 35, 86, 125, 213, ],\n  text: \"South Korea\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_country_year_12 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [27, 23, 30, 37, 52, 61, 89, 94, 118, ],\n  text: \"India\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_country_year_13 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [20, 39, 44, 41, 51, 63, 72, 57, 104, ],\n  text: \"Italy\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_country_year_14 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [18, 16, 33, 24, 55, 47, 61, 72, 97, ],\n  text: \"Netherlands\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_country_year_15 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [21, 14, 29, 19, 30, 20, 34, 34, 42, ],\n  text: \"Spain\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_country_year_16 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [6, 13, 14, 18, 23, 23, 36, 43, 56, ],\n  text: \"Denmark\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_country_year_17 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [23, 24, 18, 19, 19, 16, 28, 25, 47, ],\n  text: \"Sweden\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_country_year_18 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [8, 2, 14, 7, 10, 11, 32, 34, 47, ],\n  text: \"Taiwan\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_country_year_19 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [12, 8, 17, 14, 11, 15, 20, 17, 32, ],\n  text: \"Austria\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_country_year_20 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [6, 9, 15, 7, 10, 15, 12, 27, 43, ],\n  text: \"Finland\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_country_year_21 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [13, 8, 5, 7, 21, 14, 29, 20, 23, ],\n  text: \"Belgium\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_country_year_22 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [12, 12, 17, 11, 19, 19, 19, 9, 21, ],\n  text: \"Ireland\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_country_year_23 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [3, 7, 5, 5, 12, 9, 23, 31, 41, ],\n  text: \"Russia\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_country_year_24 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [10, 8, 12, 18, 16, 16, 10, 16, 14, ],\n  text: \"Qatar\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_country_year_25 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [9, 3, 4, 10, 8, 10, 14, 22, 24, ],\n  text: \"Poland\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_country_year_26 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [8, 8, 5, 8, 8, 7, 8, 16, 11, ],\n  text: \"Czech Republic\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_country_year_27 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [8, 7, 12, 8, 6, 5, 6, 12, 10, ],\n  text: \"Greece\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_country_year_28 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [0, 1, 2, 5, 1, 4, 11, 14, 27, ],\n  text: \"Hong Kong\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_country_year_29 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [1, 0, 4, 3, 5, 2, 7, 18, 22, ],\n  text: \"Saudi Arabia\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_country_year_30 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [3, 4, 6, 8, 8, 2, 13, 8, 7, ],\n  text: \"Portugal\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_country_year_31 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [1, 3, 2, 5, 5, 7, 8, 10, 13, ],\n  text: \"Brazil\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_country_year_32 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [5, 5, 2, 4, 8, 3, 3, 13, 10, ],\n  text: \"Iran\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_country_year_33 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [3, 3, 2, 2, 4, 4, 11, 8, 11, ],\n  text: \"Norway\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_country_year_34 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [3, 0, 2, 3, 4, 4, 7, 5, 17, ],\n  text: \"UAE\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_country_year_35 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [1, 2, 1, 1, 0, 1, 6, 18, 14, ],\n  text: \"Macau\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_country_year_36 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [4, 4, 5, 1, 9, 2, 6, 5, 4, ],\n  text: \"Turkey\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_country_year_37 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [1, 1, 6, 1, 4, 2, 6, 8, 7, ],\n  text: \"Romania\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_country_year_38 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [1, 1, 1, 2, 3, 5, 2, 7, 5, ],\n  text: \"New Zealand\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_country_year_39 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [1, 0, 1, 1, 0, 1, 4, 2, 13, ],\n  text: \"Vietnam\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_country_year_40 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [2, 3, 6, 1, 1, 2, 0, 2, 3, ],\n  text: \"Hungary\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_country_year_41 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [0, 1, 1, 1, 1, 1, 6, 3, 4, ],\n  text: \"South Africa\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_country_year_42 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [2, 4, 1, 2, 1, 1, 2, 0, 3, ],\n  text: \"Croatia\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_country_year_43 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [3, 1, 5, 1, 0, 1, 1, 0, 1, ],\n  text: \"Egypt\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_country_year_44 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [1, 1, 1, 0, 2, 2, 1, 2, 2, ],\n  text: \"Estonia\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar data_graph2020_all_country_year = [trace_graph2020_all_country_year_0, trace_graph2020_all_country_year_1, trace_graph2020_all_country_year_2, trace_graph2020_all_country_year_3, trace_graph2020_all_country_year_4, trace_graph2020_all_country_year_5, trace_graph2020_all_country_year_6, trace_graph2020_all_country_year_7, trace_graph2020_all_country_year_8, trace_graph2020_all_country_year_9, trace_graph2020_all_country_year_10, trace_graph2020_all_country_year_11, trace_graph2020_all_country_year_12, trace_graph2020_all_country_year_13, trace_graph2020_all_country_year_14, trace_graph2020_all_country_year_15, trace_graph2020_all_country_year_16, trace_graph2020_all_country_year_17, trace_graph2020_all_country_year_18, trace_graph2020_all_country_year_19, trace_graph2020_all_country_year_20, trace_graph2020_all_country_year_21, trace_graph2020_all_country_year_22, trace_graph2020_all_country_year_23, trace_graph2020_all_country_year_24, trace_graph2020_all_country_year_25, trace_graph2020_all_country_year_26, trace_graph2020_all_country_year_27, trace_graph2020_all_country_year_28, trace_graph2020_all_country_year_29, trace_graph2020_all_country_year_30, trace_graph2020_all_country_year_31, trace_graph2020_all_country_year_32, trace_graph2020_all_country_year_33, trace_graph2020_all_country_year_34, trace_graph2020_all_country_year_35, trace_graph2020_all_country_year_36, trace_graph2020_all_country_year_37, trace_graph2020_all_country_year_38, trace_graph2020_all_country_year_39, trace_graph2020_all_country_year_40, trace_graph2020_all_country_year_41, trace_graph2020_all_country_year_42, trace_graph2020_all_country_year_43, trace_graph2020_all_country_year_44];\nfixLineGraph(data_graph2020_all_country_year);\nscaleLineGraph(data_graph2020_all_country_year, 16);\nPlotly.newPlot('myplot_graph2020_all_country_year', data_graph2020_all_country_year, {margin: { l: 40, r: 0, b: 100, t: 0, pad: 4 }, showlegend: false, showticklabels: true, xaxis:{autotick: false}});\n<\/script><\/p>\n<h3>USA<\/h3>\n<p>As the United States publishes so much, the USA-only breakdown looks very similar to the combined graph, with Google, Microsoft and CMU in the lead.<\/p>\n<div id=\"myplot_graph2020_last_country_org_source_1\" style=\"height:550px;\"><\/div>\n<p><script>\nvar trace_graph2020_last_country_org_source_1_0 = {  x: [\"Google\", \"Microsoft\", \"Carnegie Mellon University\", \"Stanford University\", \"Massachusetts Institute of Technology\", \"Facebook\", \"UC Berkeley\", \"IBM\", \"DeepMind\", \"Amazon\", \"University of Washington\", \"University of Illinois\", \"Princeton University\", \"New York University\", \"Cornell University\", \"Columbia University\", \"Harvard University\", \"University of Texas at Austin\", \"University of California Los Angeles\", \"Allen Institute for Artificial Intelligence\", \"University of Pennsylvania\", \"Johns Hopkins University\", \"University of Maryland\", \"Georgia Institute of Technology\", \"University of Southern California\", \"University of Michigan\", \"University of California San Diego\", \"Duke University\", \"University of Massachusetts Amherst\", \"University of North Carolina at Chapel Hill\", \"Rutgers University\", \"Purdue University\", \"Salesforce Research\", \"Adobe Research\", \"NVIDIA 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Oxford and Cambridge are also not far behind, with Cambridge having a much larger NLP proportion than the other two.<\/p>\n<div id=\"myplot_graph2020_last_country_org_source_3\" style=\"height:550px;\"><\/div>\n<p><script>\nvar trace_graph2020_last_country_org_source_3_0 = {  x: [\"DeepMind\", \"University of Oxford\", \"University of Cambridge\", \"University College London\", \"University of Edinburgh\", \"Imperial College London\", \"Alan Turing Institute\", \"University of Sheffield\", \"University of Warwick\", \"Queen Mary University of London\", \"University of Southampton\", \"King's College London\", \"University of Manchester\", \"Cardiff University\", \"University of Liverpool\", \"University of Surrey\", \"University of London\", \"Heriot Watt University\", \"University of Bristol\", \"University of Birmingham\", \"Lancaster University\", \"Queen's University Belfast\", \"University of Nottingham\", \"University of Wolverhampton\", \"University of Exeter\", \"University of 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type: 'bar'\n};\nvar trace_graph2020_last_country_org_source_3_10 = {  x: [\"DeepMind\", \"University of Oxford\", \"University of Cambridge\", \"University College London\", \"University of Edinburgh\", \"Imperial College London\", \"Alan Turing Institute\", \"University of Sheffield\", \"University of Warwick\", \"Queen Mary University of London\", \"University of Southampton\", \"King's College London\", \"University of Manchester\", \"Cardiff University\", \"University of Liverpool\", \"University of Surrey\", \"University of London\", \"Heriot Watt University\", \"University of Bristol\", \"University of Birmingham\", \"Lancaster University\", \"Queen's University Belfast\", \"University of Nottingham\", \"University of Wolverhampton\", \"University of Exeter\", \"University of Aberdeen\", \"University of Essex\", \"Invenia Labs\", \"University of Bath\", \"University of Sussex\", \"University of Leeds\", \"University of Glasgow\", \"University of Lincoln\", \"The Open 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trace_graph2020_last_country_org_source_3_9, trace_graph2020_last_country_org_source_3_10];\nfixBarGraph(data_graph2020_last_country_org_source_3);\nscaleBarGraph(data_graph2020_last_country_org_source_3, 32);\nPlotly.newPlot('myplot_graph2020_last_country_org_source_3', data_graph2020_last_country_org_source_3, {barmode: 'stack', margin: { l: 30, r: 0, b: 270, t: 20, pad: 4 }});\n<\/script><\/p>\n<h3>Germany<\/h3>\n<p>Rober Bosch GmbH is the top publishing organisation in Germany this year, with a very impressive NeurIPS result. Darmstadt comes in second as the top university and with a much larger NLP output. T\u00fcbingen ranks third with almost all papers in ML conferences.<\/p>\n<div id=\"myplot_graph2020_last_country_org_source_4\" style=\"height:550px;\"><\/div>\n<p><script>\nvar trace_graph2020_last_country_org_source_4_0 = {  x: [\"Robert Bosch GmbH\", \"Technische Universita\u0308t Darmstadt\", \"University of Tu\u0308bingen\", \"Saarland University\", \"Technical University of Munich\", \"University of Munich\", \"Max Planck Institute for Intelligent Systems\", \"University of Stuttgart\", \"University of Mannheim\", \"MMax Planck Institute for Informatics\", \"Universit\u00e4t Potsdam\", \"University of Heidelberg\", \"Technische Universit\u00e4t Berlin (TUB)\", \"University of Freiburg\", \"Universita\u0308t Leipzig\", \"Paderborn University\", \"RWTH Aachen University\", \"DFKI\", \"German Research Center for Artificial Intelligence\", \"Max Planck Institute\", \"Karlsruhe Institute of Technology KIT\", \"University of Bonn\", \"University of D\u00fcsseldorf\", 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32);\nPlotly.newPlot('myplot_graph2020_last_country_org_source_4', data_graph2020_last_country_org_source_4, {barmode: 'stack', margin: { l: 30, r: 0, b: 270, t: 20, pad: 4 }});\n<\/script><\/p>\n<h3>Canada<\/h3>\n<p>In Canada, University of Toronto is the top ranking organisation in terms of publishing. Vector Institute ranks second and McGill third.<\/p>\n<div id=\"myplot_graph2020_last_country_org_source_5\" style=\"height:550px;\"><\/div>\n<p><script>\nvar trace_graph2020_last_country_org_source_5_0 = {  x: [\"University of Toronto\", \"Vector Institute\", \"McGill University\", \"University of Alberta\", \"Universite\u0301 de Montre\u0301al\", \"University of British Columbia\", \"Montreal Institute of Learning Algorithms\", \"University of Waterloo\", \"Simon Fraser University\", \"York University\", \"Borealis AI\", \"University of Ottawa\", \"National Research Council Canada\", \"Queen's University\", \"University of Western Ontario\", \"Mila McGill University\", \"Carleton University\", \"Canadian Institute for Advanced Research\", \"\u00c9cole Polytechnique de Montr\u00e9al\", \"University of Guelph\", \"Concordia University\", \"Li Ka Shing Knowledge Institute\", \"Ryerson University\", \"McMaster University\", \"Dalhousie University\", \"University of Saskatchewan\", 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University\", \"University of Alberta\", \"Universite\u0301 de Montre\u0301al\", \"University of British Columbia\", \"Montreal Institute of Learning Algorithms\", \"University of Waterloo\", \"Simon Fraser University\", \"York University\", \"Borealis AI\", \"University of Ottawa\", \"National Research Council Canada\", \"Queen's University\", \"University of Western Ontario\", \"Mila McGill University\", \"Carleton University\", \"Canadian Institute for Advanced Research\", \"\u00c9cole Polytechnique de Montr\u00e9al\", \"University of Guelph\", \"Concordia University\", \"Li Ka Shing Knowledge Institute\", \"Ryerson University\", \"McMaster University\", \"Dalhousie University\", \"University of Saskatchewan\", \"Ontario Tech University\", \"ZTE Corporation\", \"Brock University\", \"JP Morgan AI Research\", \"University of Manitoba\", \"University of Calgary\", \"Universit\u00e9 Laval\", \"Universit\u00e9 de Moncton\", \"University of Montreal\", ],\n  y: [30, 18, 15, 11, 18, 10, 8, 8, 5, 2, 2, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, ],\n  name: 'NeurIPS',\n  type: 'bar'\n};\nvar trace_graph2020_last_country_org_source_5_4 = {  x: [\"University of Toronto\", \"Vector Institute\", \"McGill University\", \"University of Alberta\", \"Universite\u0301 de Montre\u0301al\", \"University of British Columbia\", \"Montreal Institute of Learning Algorithms\", \"University of Waterloo\", \"Simon Fraser University\", \"York University\", \"Borealis AI\", \"University of Ottawa\", \"National Research Council Canada\", \"Queen's University\", \"University of Western Ontario\", \"Mila McGill University\", \"Carleton University\", \"Canadian Institute for Advanced Research\", \"\u00c9cole Polytechnique de Montr\u00e9al\", \"University of Guelph\", \"Concordia University\", \"Li Ka Shing Knowledge Institute\", \"Ryerson University\", \"McMaster University\", \"Dalhousie University\", \"University of Saskatchewan\", \"Ontario Tech University\", \"ZTE Corporation\", \"Brock University\", \"JP Morgan AI Research\", \"University of Manitoba\", \"University of Calgary\", \"Universit\u00e9 Laval\", \"Universit\u00e9 de Moncton\", \"University of Montreal\", ],\n  y: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ],\n  name: 'CL',\n  type: 'bar'\n};\nvar trace_graph2020_last_country_org_source_5_5 = {  x: [\"University of Toronto\", \"Vector Institute\", \"McGill University\", \"University of Alberta\", \"Universite\u0301 de Montre\u0301al\", \"University of British Columbia\", \"Montreal Institute of Learning Algorithms\", \"University of Waterloo\", \"Simon Fraser University\", \"York University\", \"Borealis AI\", \"University of Ottawa\", \"National Research Council Canada\", \"Queen's University\", \"University of Western Ontario\", \"Mila McGill University\", \"Carleton University\", \"Canadian Institute for Advanced Research\", \"\u00c9cole Polytechnique de Montr\u00e9al\", \"University of Guelph\", \"Concordia University\", \"Li Ka Shing Knowledge Institute\", \"Ryerson University\", \"McMaster University\", \"Dalhousie University\", \"University of Saskatchewan\", \"Ontario Tech University\", \"ZTE Corporation\", \"Brock University\", \"JP Morgan AI Research\", \"University of Manitoba\", \"University of Calgary\", \"Universit\u00e9 Laval\", \"Universit\u00e9 de Moncton\", \"University of Montreal\", ],\n  y: [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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_graph2020_last_country_org_source_5_6 = {  x: [\"University of Toronto\", \"Vector Institute\", \"McGill University\", \"University of Alberta\", \"Universite\u0301 de Montre\u0301al\", \"University of British Columbia\", \"Montreal Institute of Learning Algorithms\", \"University of Waterloo\", \"Simon Fraser University\", \"York University\", \"Borealis AI\", \"University of Ottawa\", \"National Research Council Canada\", \"Queen's University\", \"University of Western Ontario\", \"Mila McGill University\", \"Carleton University\", \"Canadian Institute for Advanced Research\", \"\u00c9cole Polytechnique de Montr\u00e9al\", \"University of Guelph\", \"Concordia University\", \"Li Ka Shing Knowledge Institute\", \"Ryerson University\", \"McMaster University\", \"Dalhousie University\", \"University of Saskatchewan\", \"Ontario Tech University\", \"ZTE Corporation\", \"Brock University\", \"JP Morgan AI Research\", \"University of Manitoba\", \"University of Calgary\", \"Universit\u00e9 Laval\", \"Universit\u00e9 de Moncton\", \"University of Montreal\", ],\n  y: [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ],\n  name: 'CoNLL',\n  type: 'bar'\n};\nvar trace_graph2020_last_country_org_source_5_7 = {  x: [\"University of Toronto\", \"Vector Institute\", \"McGill University\", \"University of Alberta\", \"Universite\u0301 de Montre\u0301al\", \"University of British Columbia\", \"Montreal Institute of Learning Algorithms\", \"University of Waterloo\", \"Simon Fraser University\", \"York University\", \"Borealis AI\", \"University of Ottawa\", \"National Research Council Canada\", \"Queen's University\", \"University of Western Ontario\", \"Mila McGill University\", \"Carleton University\", \"Canadian Institute for Advanced Research\", \"\u00c9cole Polytechnique de Montr\u00e9al\", \"University of Guelph\", \"Concordia University\", \"Li Ka Shing Knowledge Institute\", \"Ryerson University\", \"McMaster University\", \"Dalhousie University\", \"University of Saskatchewan\", \"Ontario Tech University\", \"ZTE Corporation\", \"Brock University\", \"JP Morgan AI Research\", \"University of Manitoba\", \"University of Calgary\", \"Universit\u00e9 Laval\", \"Universit\u00e9 de Moncton\", \"University of Montreal\", ],\n  y: [1, 0, 1, 3, 0, 6, 1, 3, 2, 3, 1, 0, 2, 2, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, ],\n  name: 'COLING',\n  type: 'bar'\n};\nvar trace_graph2020_last_country_org_source_5_8 = {  x: [\"University of Toronto\", \"Vector Institute\", \"McGill University\", \"University of Alberta\", \"Universite\u0301 de Montre\u0301al\", \"University of British Columbia\", \"Montreal Institute of Learning Algorithms\", \"University of Waterloo\", \"Simon Fraser University\", \"York University\", \"Borealis AI\", \"University of Ottawa\", \"National Research Council Canada\", \"Queen's University\", \"University of Western Ontario\", \"Mila McGill University\", \"Carleton University\", \"Canadian Institute for Advanced Research\", \"\u00c9cole Polytechnique de Montr\u00e9al\", \"University of Guelph\", \"Concordia University\", \"Li Ka Shing Knowledge Institute\", \"Ryerson University\", \"McMaster University\", \"Dalhousie University\", \"University of Saskatchewan\", \"Ontario Tech University\", \"ZTE Corporation\", \"Brock University\", \"JP Morgan AI Research\", \"University of Manitoba\", \"University of Calgary\", \"Universit\u00e9 Laval\", \"Universit\u00e9 de Moncton\", \"University of Montreal\", ],\n  y: [0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ],\n  name: 'AACL',\n  type: 'bar'\n};\nvar trace_graph2020_last_country_org_source_5_9 = {  x: [\"University of Toronto\", \"Vector Institute\", \"McGill University\", \"University of Alberta\", \"Universite\u0301 de Montre\u0301al\", \"University of British Columbia\", \"Montreal Institute of Learning Algorithms\", \"University of Waterloo\", \"Simon Fraser University\", \"York University\", \"Borealis AI\", \"University of Ottawa\", \"National Research Council Canada\", \"Queen's University\", \"University of Western Ontario\", \"Mila McGill University\", \"Carleton University\", \"Canadian Institute for Advanced Research\", \"\u00c9cole Polytechnique de Montr\u00e9al\", \"University of Guelph\", \"Concordia University\", \"Li Ka Shing Knowledge Institute\", \"Ryerson University\", \"McMaster University\", \"Dalhousie University\", \"University of Saskatchewan\", \"Ontario Tech University\", \"ZTE Corporation\", \"Brock University\", \"JP Morgan AI Research\", \"University of Manitoba\", \"University of Calgary\", \"Universit\u00e9 Laval\", \"Universit\u00e9 de Moncton\", \"University of Montreal\", ],\n  y: [5, 2, 7, 1, 3, 2, 6, 1, 1, 1, 0, 3, 0, 1, 0, 2, 0, 0, 1, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ],\n  name: 'EMNLP',\n  type: 'bar'\n};\nvar trace_graph2020_last_country_org_source_5_10 = {  x: [\"University of Toronto\", \"Vector Institute\", \"McGill University\", \"University of Alberta\", \"Universite\u0301 de Montre\u0301al\", \"University of British Columbia\", \"Montreal Institute of Learning Algorithms\", \"University of Waterloo\", \"Simon Fraser University\", \"York University\", \"Borealis AI\", \"University of Ottawa\", \"National Research Council Canada\", \"Queen's University\", \"University of Western Ontario\", \"Mila McGill University\", \"Carleton University\", \"Canadian Institute for Advanced Research\", \"\u00c9cole Polytechnique de Montr\u00e9al\", \"University of Guelph\", \"Concordia University\", \"Li Ka Shing Knowledge Institute\", \"Ryerson University\", \"McMaster University\", \"Dalhousie University\", \"University of Saskatchewan\", \"Ontario Tech University\", \"ZTE Corporation\", \"Brock University\", \"JP Morgan AI Research\", \"University of Manitoba\", \"University of Calgary\", \"Universit\u00e9 Laval\", \"Universit\u00e9 de Moncton\", \"University of Montreal\", ],\n  y: [0, 0, 3, 2, 2, 0, 0, 4, 2, 2, 0, 0, 2, 3, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, ],\n  name: 'ACL',\n  type: 'bar'\n};\nvar data_graph2020_last_country_org_source_5 = [trace_graph2020_last_country_org_source_5_0, trace_graph2020_last_country_org_source_5_1, trace_graph2020_last_country_org_source_5_2, trace_graph2020_last_country_org_source_5_3, trace_graph2020_last_country_org_source_5_4, trace_graph2020_last_country_org_source_5_5, trace_graph2020_last_country_org_source_5_6, trace_graph2020_last_country_org_source_5_7, trace_graph2020_last_country_org_source_5_8, trace_graph2020_last_country_org_source_5_9, trace_graph2020_last_country_org_source_5_10];\nfixBarGraph(data_graph2020_last_country_org_source_5);\nscaleBarGraph(data_graph2020_last_country_org_source_5, 32);\nPlotly.newPlot('myplot_graph2020_last_country_org_source_5', data_graph2020_last_country_org_source_5, {barmode: 'stack', margin: { l: 30, r: 0, b: 270, t: 20, pad: 4 }});\n<\/script><\/p>\n<h2>Topic Similarity<\/h2>\n<p>Just for fun, I also ran the papers through LDA and then visualised them using t-SNE.<\/p>\n<p>Looking at the top publishing organisations, there is a clear geographic separation happening on the left and right side of the graph. In the middle we have global companies, such as Google, Amazon and Microsoft.<\/p>\n<p><a href=\"https:\/\/www.marekrei.com\/blog\/wp-content\/uploads\/2021\/02\/tsne_organisations.png\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/www.marekrei.com\/blog\/wp-content\/uploads\/2021\/02\/tsne_organisations.png\" alt=\"\" width=\"825\" height=\"434\" class=\"aligncenter size-full wp-image-1762\" srcset=\"https:\/\/www.marekrei.com\/blog\/wp-content\/uploads\/2021\/02\/tsne_organisations.png 825w, https:\/\/www.marekrei.com\/blog\/wp-content\/uploads\/2021\/02\/tsne_organisations-150x79.png 150w, https:\/\/www.marekrei.com\/blog\/wp-content\/uploads\/2021\/02\/tsne_organisations-300x158.png 300w, https:\/\/www.marekrei.com\/blog\/wp-content\/uploads\/2021\/02\/tsne_organisations-768x404.png 768w\" sizes=\"auto, (max-width: 825px) 100vw, 825px\" \/><\/a><\/p>\n<p>Another LDA graph for the top publishing authors of 2020. If you spot any interesting clusters here, let me know in the comments as well.<\/p>\n<p><a href=\"https:\/\/www.marekrei.com\/blog\/wp-content\/uploads\/2021\/02\/tsne_authors.png\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/www.marekrei.com\/blog\/wp-content\/uploads\/2021\/02\/tsne_authors.png\" alt=\"\" width=\"821\" height=\"436\" class=\"aligncenter size-full wp-image-1763\" srcset=\"https:\/\/www.marekrei.com\/blog\/wp-content\/uploads\/2021\/02\/tsne_authors.png 821w, https:\/\/www.marekrei.com\/blog\/wp-content\/uploads\/2021\/02\/tsne_authors-150x80.png 150w, https:\/\/www.marekrei.com\/blog\/wp-content\/uploads\/2021\/02\/tsne_authors-300x159.png 300w, https:\/\/www.marekrei.com\/blog\/wp-content\/uploads\/2021\/02\/tsne_authors-768x408.png 768w\" sizes=\"auto, (max-width: 821px) 100vw, 821px\" \/><\/a><\/p>\n<p>Finally, plotting the countries. Geographic proximity seems to carry over directly to paper similarity. One possible explanation is that neighbouring countries are more likely to publish together, which would make their paper collections also more similar.<\/p>\n<p><a href=\"https:\/\/www.marekrei.com\/blog\/wp-content\/uploads\/2021\/02\/tsne_countries.png\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/www.marekrei.com\/blog\/wp-content\/uploads\/2021\/02\/tsne_countries.png\" alt=\"\" width=\"816\" height=\"426\" class=\"aligncenter size-full wp-image-1764\" srcset=\"https:\/\/www.marekrei.com\/blog\/wp-content\/uploads\/2021\/02\/tsne_countries.png 816w, https:\/\/www.marekrei.com\/blog\/wp-content\/uploads\/2021\/02\/tsne_countries-150x78.png 150w, https:\/\/www.marekrei.com\/blog\/wp-content\/uploads\/2021\/02\/tsne_countries-300x157.png 300w, https:\/\/www.marekrei.com\/blog\/wp-content\/uploads\/2021\/02\/tsne_countries-768x401.png 768w\" sizes=\"auto, (max-width: 816px) 100vw, 816px\" \/><\/a><\/p>\n<h2>Keywords<\/h2>\n<p>As a new addition, I thought it might be informative to also plot the usage of different keywords in papers. Here the y-axis represents the proportion of papers that contain that specific keyword, as opposed to an absolute count. <\/p>\n<p>It is interesting to see how the word \"neural\" has increased in popularity over the years, now plateauing at over 80% of all the papers. \"Recurrent\" and \"convolutional\" have taken downward directions, while \"transformer\" has a steep rising trajectory. It is nice to see a gradual rise of \"github\", which implies that more papers are making their source code available.<\/p>\n<div id=\"myplot_graph2020_all_keyword_year\"><\/div>\n<p><script>\nvar trace_graph2020_all_keyword_year_0 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [0.11735261401557286, 0.13508557457212714, 0.20113851992409867, 0.3198900091659028, 0.610989010989011, 0.7216142270861833, 0.8145126663804207, 0.8410618858817446, 0.8333988727225062, ],\n  text: \"neural\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_keyword_year_1 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [0.185761957730812, 0.20904645476772615, 0.2547438330170778, 0.29880843263061413, 0.4084249084249084, 0.5034199726402189, 0.5978960927436668, 0.631098086536804, 0.6593262550793026, ],\n  text: \"deep\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_keyword_year_2 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [0.21579532814238042, 0.2047677261613692, 0.18927893738140417, 0.1998166819431714, 0.1783882783882784, 0.1822845417236662, 0.16423357664233576, 0.15807619375969661, 0.14942980731419583, ],\n  text: \"bayesian\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_keyword_year_3 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [0.02335928809788654, 0.03178484107579462, 0.03320683111954459, 0.03712190650779102, 0.05787545787545788, 0.11764705882352941, 0.18656075568913696, 0.24943975176693672, 0.27054659850570195, ],\n  text: \"adversarial\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_keyword_year_4 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [0.067853170189099, 0.061124694376528114, 0.05075901328273245, 0.06003666361136572, 0.06373626373626373, 0.1374829001367989, 0.18784886217260627, 0.20582658162385795, 0.2152313540437803, ],\n  text: \"reinforcement\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_keyword_year_5 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [0.6012235817575083, 0.5837408312958435, 0.5896584440227703, 0.5934922089825848, 0.5721611721611721, 0.5403556771545828, 0.5249033920137398, 0.5087053956214446, 0.5240529558264517, ],\n  text: \"graph\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_keyword_year_6 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [0.027808676307007785, 0.030562347188264057, 0.04601518026565465, 0.09624197983501374, 0.22857142857142856, 0.28214774281805743, 0.32116788321167883, 0.27546974659541457, 0.2219163717394154, ],\n  text: \"recurrent\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_keyword_year_7 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [0.013904338153503892, 0.029339853300733496, 0.05502846299810247, 0.1076993583868011, 0.20805860805860807, 0.2814637482900137, 0.3473593817088879, 0.35131873814859504, 0.32376458251409096, ],\n  text: \"convolutional\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_keyword_year_8 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [0.00389321468298109, 0.003667481662591687, 0.0009487666034155598, 0.006416131989000917, 0.0062271062271062275, 0.009917920656634747, 0.03628166595105196, 0.13463196000689537, 0.25298204220736664, ],\n  text: \"transformer\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_keyword_year_9 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [0.26807563959955505, 0.254278728606357, 0.24573055028462998, 0.2598533455545371, 0.2945054945054945, 0.27325581395348836, 0.29991412623443536, 0.28374418203757973, 0.2718573862891598, ],\n  text: \"translation\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_keyword_year_10 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [0.22580645161290322, 0.21026894865525672, 0.20825426944971537, 0.2076076993583868, 0.20036630036630038, 0.15595075239398085, 0.13374838986689566, 0.10825719703499397, 0.09476995674400314, ],\n  text: \"parsing\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_keyword_year_11 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [0.028921023359288096, 0.04462102689486552, 0.048861480075901326, 0.05682859761686526, 0.07545787545787545, 0.10225718194254446, 0.11206526406182911, 0.12859851749698328, 0.11783982173286145, ],\n  text: \"mnist\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_keyword_year_12 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [0.04560622914349277, 0.0488997555012225, 0.06878557874762808, 0.08249312557286893, 0.07838827838827839, 0.06155950752393981, 0.0631172176899957, 0.04619893121875539, 0.04509109975095032, ],\n  text: \"twitter\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_keyword_year_13 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [0.014460511679644048, 0.040342298288508556, 0.06309297912713473, 0.14757103574702107, 0.2402930402930403, 0.32181942544459646, 0.43022756547874624, 0.49388036545423203, 0.5454187966968148, ],\n  text: \"github\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar data_graph2020_all_keyword_year = [trace_graph2020_all_keyword_year_0, trace_graph2020_all_keyword_year_1, trace_graph2020_all_keyword_year_2, trace_graph2020_all_keyword_year_3, trace_graph2020_all_keyword_year_4, trace_graph2020_all_keyword_year_5, trace_graph2020_all_keyword_year_6, trace_graph2020_all_keyword_year_7, trace_graph2020_all_keyword_year_8, trace_graph2020_all_keyword_year_9, trace_graph2020_all_keyword_year_10, trace_graph2020_all_keyword_year_11, trace_graph2020_all_keyword_year_12, trace_graph2020_all_keyword_year_13];\nPlotly.newPlot('myplot_graph2020_all_keyword_year', data_graph2020_all_keyword_year, {margin: { l: 30, r: 0, b: 100, t: 0, pad: 4 }, showlegend: false, showticklabels: true, xaxis:{autotick: false}});\n<\/script><\/p>\n<p>Just looking at the keyword \"neural\", we see that almost all papers in ICLR and NeurIPS are mentioning it. The proportion is consistently high also in other conferences, although not quite to the same level. Interestingly, EMNLP, CoNLL and CL have actually reduced the proportion of neural papers in recent years.<\/p>\n<div id=\"myplot_graph2020_all_source_year_neural\"><\/div>\n<p><script>\nvar trace_graph2020_all_source_year_neural_0 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [0.03208556149732621, 0.06097560975609756, 0.18181818181818182, 0.42452830188679247, 0.5487804878048781, 0.7649006622516556, 0.84251968503937, 0.8469696969696969, 0.8521850899742931, ],\n  text: \"ACL\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_source_year_neural_1 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [0.04316546762589928, 0.13658536585365855, 0.19911504424778761, 0.38782051282051283, 0.6098484848484849, 0.7832817337461301, 0.8761384335154827, 0.8575624082232012, 0.7723035952063915, ],\n  text: \"EMNLP\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar 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\"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [0.045180722891566265, 0.0, 0.1583710407239819, 0.0, 0.5727002967359051, 0.0, 0.7545454545454545, 0.0, 0.7843137254901961, ],\n  text: \"COLING\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_source_year_neural_6 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [0.0, 0.16, 0.3, 0.42105263157894735, 0.6129032258064516, 0.7555555555555555, 0.8928571428571429, 0.9484536082474226, 0.7735849056603774, ],\n  text: \"CoNLL\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_source_year_neural_7 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [0.0, 0.02857142857142857, 0.16279069767441862, 0.3333333333333333, 0.5641025641025641, 0.6756756756756757, 0.8541666666666666, 0.8888888888888888, 0.8913043478260869, ],\n  text: \"TACL\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_source_year_neural_8 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [0.027777777777777776, 0.08571428571428572, 0.14285714285714285, 0.21212121212121213, 0.3225806451612903, 0.35714285714285715, 0.5, 0.8333333333333334, 0.7647058823529411, ],\n  text: \"CL\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_source_year_neural_9 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [0.20652173913043478, 0.23333333333333334, 0.2871046228710462, 0.3200992555831266, 0.9824253075571178, 0.9955817378497791, 0.9990089197224975, 0.9964961457603364, 0.9957850368809273, ],\n  text: \"NeurIPS\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_source_year_neural_10 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [0.1440329218106996, 0.17314487632508835, 0.1774193548387097, 0.2074074074074074, 0.38198757763975155, 0.5253456221198156, 0.6167471819645732, 0.6403622250970246, 0.6608294930875576, ],\n  text: \"ICML\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_source_year_neural_11 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [0.0, 0.5217391304347826, 0.7647058823529411, 1.0, 1.0, 1.0, 0.9970238095238095, 0.9920318725099602, 0.9941775836972343, ],\n  text: \"ICLR\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_source_year_neural_12 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [0.18971061093247588, 0.06435643564356436, 0.15577889447236182, 0.22490706319702602, 0.4032846715328467, 0.45539906103286387, 0.6574172892209178, 0.6939843068875327, 0.7291925465838509, ],\n  text: \"AAAI\",\n  type: 'scatter',\nhoverinfo: 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papers instead. For EMNLP, the trend is more stable. For most conferences, the \"bayesian\" proportion seems to have a slight downward trend overall, with CoNLL and ICLR being the exceptions.<\/p>\n<div id=\"myplot_graph2020_all_source_year_bayesian\"><\/div>\n<p><script>\nvar trace_graph2020_all_source_year_bayesian_0 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [0.15508021390374332, 0.14329268292682926, 0.0944055944055944, 0.10062893081761007, 0.07621951219512195, 0.08940397350993377, 0.05249343832020997, 0.06515151515151515, 0.060411311053984576, ],\n  text: \"ACL\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_source_year_bayesian_1 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [0.18705035971223022, 0.13658536585365855, 0.14601769911504425, 0.11858974358974358, 0.12121212121212122, 0.08978328173374613, 0.058287795992714025, 0.05286343612334802, 0.05725699067909454, ],\n  text: 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'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_source_year_bayesian_5 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [0.0783132530120482, 0.0, 0.11312217194570136, 0.0, 0.0830860534124629, 0.0, 0.06363636363636363, 0.0, 0.0392156862745098, ],\n  text: \"COLING\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_source_year_bayesian_6 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [0.0, 0.12, 0.2, 0.13157894736842105, 0.12903225806451613, 0.08888888888888889, 0.05357142857142857, 0.08247422680412371, 0.1509433962264151, ],\n  text: \"CoNLL\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_source_year_bayesian_7 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [0.0, 0.17142857142857143, 0.20930232558139536, 0.30952380952380953, 0.07692307692307693, 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showlegend: false, showticklabels: true, xaxis:{autotick: false}});\n<\/script><\/p>\n<p>Distribution of \"github\" shows indeed that the practice of releasing source code is becoming more common in all the conferences. The NLP conferences are still doing much better in this respect, compared to all the ML conferences, with TACL and EMNLP having close to 80% of the papers mentioning github. In contrast, ICLR is around 60% and AAAI is the lowest with 40%.<\/p>\n<div id=\"myplot_graph2020_all_source_year_github\"><\/div>\n<p><script>\nvar trace_graph2020_all_source_year_github_0 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [0.026737967914438502, 0.054878048780487805, 0.0944055944055944, 0.18867924528301888, 0.3353658536585366, 0.48013245033112584, 0.6167979002624672, 0.6878787878787879, 0.7519280205655527, ],\n  text: \"ACL\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_source_year_github_1 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [0.02158273381294964, 0.06341463414634146, 0.09734513274336283, 0.266025641025641, 0.4128787878787879, 0.49226006191950467, 0.604735883424408, 0.6857562408223201, 0.7869507323568575, ],\n  text: \"EMNLP\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar 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\"TACL\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_source_year_github_8 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [0.0, 0.0, 0.0, 0.09090909090909091, 0.0967741935483871, 0.10714285714285714, 0.3235294117647059, 0.6666666666666666, 0.7058823529411765, ],\n  text: \"CL\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_source_year_github_9 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [0.002717391304347826, 0.025, 0.0413625304136253, 0.12158808933002481, 0.17398945518453426, 0.27982326951399117, 0.3587710604558969, 0.416958654519972, 0.49209694415173866, ],\n  text: \"NeurIPS\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_source_year_github_10 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [0.00411522633744856, 0.028268551236749116, 0.05806451612903226, 0.12962962962962962, 0.16770186335403728, 0.2350230414746544, 0.31239935587761675, 0.4126778783958603, 0.4294930875576037, ],\n  text: \"ICML\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_source_year_github_11 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [0.0, 0.08695652173913043, 0.08823529411764706, 0.35714285714285715, 0.3125, 0.5454545454545454, 0.5357142857142857, 0.549800796812749, 0.6055312954876274, ],\n  text: \"ICLR\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar trace_graph2020_all_source_year_github_12 = {  x: [\"2012\", \"2013\", \"2014\", \"2015\", \"2016\", \"2017\", \"2018\", \"2019\", \"2020\", ],\n  y: [0.003215434083601286, 0.0, 0.020100502512562814, 0.03345724907063197, 0.07664233576642336, 0.13458528951486698, 0.24759871931696906, 0.2946817785527463, 0.38633540372670805, ],\n  text: \"AAAI\",\n  type: 'scatter',\nhoverinfo: \"text\"\n};\nvar data_graph2020_all_source_year_github = [trace_graph2020_all_source_year_github_0, trace_graph2020_all_source_year_github_1, trace_graph2020_all_source_year_github_2, trace_graph2020_all_source_year_github_3, trace_graph2020_all_source_year_github_4, trace_graph2020_all_source_year_github_5, trace_graph2020_all_source_year_github_6, trace_graph2020_all_source_year_github_7, trace_graph2020_all_source_year_github_8, trace_graph2020_all_source_year_github_9, trace_graph2020_all_source_year_github_10, trace_graph2020_all_source_year_github_11, trace_graph2020_all_source_year_github_12];\nremoveZeroEntriesOfLineGraph(data_graph2020_all_source_year_github);\nPlotly.newPlot('myplot_graph2020_all_source_year_github', data_graph2020_all_source_year_github, {margin: { l: 30, r: 0, b: 100, t: 0, pad: 4 }, showlegend: false, showticklabels: true, xaxis:{autotick: false}});\n<\/script><\/p>\n<h2>Fun Facts<\/h2>\n<p>And a couple more random facts to finish:<\/p>\n<ul>\n<li>Most authors on a single paper: <a href=\"https:\/\/www.aclweb.org\/anthology\/2020.tacl-1.36.pdf\">Task-Oriented Dialogue as Dataflow Synthesis<\/a> in TACL with 45 authors. This doesn't quite take the all-time record but gets an honorable second position.<\/li>\n<li>The longest paper title: <a href=\"https:\/\/www.aclweb.org\/anthology\/2020.acl-main.96.pdf\">Code-Switching Patterns Can Be an Effective Route to Improve Performance of Downstream NLP Applications: A Case Study of Humour, Sarcasm and Hate Speech Detection<\/a> at ACL 2020.<\/li>\n<li>The shortest paper title: <a href=\"https:\/\/papers.nips.cc\/paper\/2020\/file\/228669109aa3ab1b4ec06b7722efb105-Paper.pdf\">Ode to an ODE<\/a> at NeurIPS 2020.\n<\/ul>\n<p>That's it for this year. I hope you found something interesting. The full processed dataset of published papers is available in this repository: <a href=\"https:\/\/github.com\/marekrei\/ml_nlp_paper_data\">https:\/\/github.com\/marekrei\/ml_nlp_paper_data<\/a><\/p>\n<p>Have a great year of publishing excellent work! May reviewer #2 be merciful to you. And if you invent AGI in the next 12 months, make it benevolent.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>I ran my paper analysis pipeline once again in order to get statistics for 2020. It certainly was an unusual year. 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