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"paper_id": "2020",
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"title": "Emily Alsentzer, MIT (USA) Amittai Axelrod, DiDi Labs (USA) William Boag, MIT (USA) Anneke Buffone, Facebook (USA) Aleksandr Drozd, RIKEN (Japan)",
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"text": "Publication of negative results is difficult in most fields, and the current focus on benchmarkdriven performance improvement exacerbates this situation and implicitly discourages hypothesis-driven research. As a result, the development of NLP models often devolves into a product of tinkering and tweaking, rather than science. Furthermore, it increases the time, effort, and carbon emissions spent on developing and tuning models, as the researchers have little opportunity to learn from what has already been tried and failed.",
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"section": "Introduction",
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"text": "Historically, this tendency is hard to combat. ACL 2010 invited negative results as a special type of research paper submissions 1 , but received too few submissions and did not continue with it. The Journal for Interesting Negative Results in NLP and ML 2 has only produced one issue in 2008.",
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"section": "Introduction",
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"text": "However, the tide may be turning. The first iteration of the Workshop on Insights from Negative Results attracted 35 submissions and 11 presentation requests for papers accepted to \"Findings of EMNLP\". Moreover, we are not alone: an independent workshop \"I can't believe it's not better!\" is held at NeurIPS 2020 3 .",
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"section": "Introduction",
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"text": "We invited submissions with many kinds of negative results, with the hope that they could yield useful insights and provide a much-needed reality check on the successes of deep learning models in NLP. In particular, we solicited the following types of contributions:",
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"section": "Introduction",
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"text": "\u2022 broadly applicable recommendations for training/fine-tuning, especially if X that didn't work is something that many practitioners would think reasonable to try, and if the demonstration of X's failure is accompanied by some explanation/hypothesis;",
"cite_spans": [],
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"section": "Introduction",
"sec_num": null
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"text": "\u2022 ablation studies of components in previously proposed models, showing that their contributions are different from what was initially reported;",
"cite_spans": [],
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"eq_spans": [],
"section": "Introduction",
"sec_num": null
},
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"text": "\u2022 datasets or probing tasks showing that previous approaches do not generalize to other domains or language phenomena;",
"cite_spans": [],
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"eq_spans": [],
"section": "Introduction",
"sec_num": null
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"text": "\u2022 trivial baselines that work suspiciously well for a given task/dataset;",
"cite_spans": [],
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"section": "Introduction",
"sec_num": null
},
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"text": "\u2022 cross-lingual studies showing that a technique X is only successful for a certain language or language family;",
"cite_spans": [],
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"section": "Introduction",
"sec_num": null
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"text": "\u2022 experiments on (in)stability of the previously published results due to hardware, random initializations, preprocessing pipeline components, etc;",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": null
},
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"text": "\u2022 theoretical arguments and/or proofs for why X should not be expected to work.",
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"section": "Introduction",
"sec_num": null
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"text": "In terms of topics, 15 papers from our submission pool discussed \"great ideas that didn't work\", 12 dealt with the issues of generalizability, 5 were on the topic of \"right for the wrong reasons\", and 2 more papers focused on reproducibility issues. We accepted 18 short papers (51.4% acceptance rate) and granted 5 presentation requests for Findings papers.",
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"section": "Introduction",
"sec_num": null
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"text": "We hope that this event will be the first of many reality-check discussions on progress in NLP. If we do not talk about things that do not work, it is harder to see what the biggest problems are and where the community effort is the most needed. ",
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"section": "Introduction",
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"text": "https://mirror.aclweb.org/acl2010/papers.html 2 http://jinr.site.uottawa.ca/ 3 https://i-cant-believe-its-not-better.github.io/",
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"ref_id": "b0",
"title": "Knowledge Graphs be used to Answer Boolean Questions? A. It's complicated! Daria Dzendzik, Carl Vogel and Jennifer Foster",
"authors": [
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"first": "Q",
"middle": [],
"last": "Can",
"suffix": ""
}
],
"year": null,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
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"raw_text": "Q. Can Knowledge Graphs be used to Answer Boolean Questions? A. It's complicated! Daria Dzendzik, Carl Vogel and Jennifer Foster . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6",
"links": null
},
"BIBREF1": {
"ref_id": "b1",
"title": "How Far Can We Go with Data Selection? A Case Study on Semantic Sequence Tagging Tasks Samuel Louvan and Bernardo Magnini",
"authors": [],
"year": null,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
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"raw_text": "How Far Can We Go with Data Selection? A Case Study on Semantic Sequence Tagging Tasks Samuel Louvan and Bernardo Magnini . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15",
"links": null
},
"BIBREF2": {
"ref_id": "b2",
"title": "Evaluating the Effectiveness of Efficient Neural Architecture Search for Sentence-Pair Tasks Ansel MacLaughlin",
"authors": [
{
"first": "Jwala",
"middle": [],
"last": "Dhamala",
"suffix": ""
},
{
"first": "Anoop",
"middle": [],
"last": "Kumar",
"suffix": ""
},
{
"first": "Sriram",
"middle": [],
"last": "Venkatapathy",
"suffix": ""
},
{
"first": "Ragav",
"middle": [],
"last": "Venkatesan",
"suffix": ""
},
{
"first": "Rahul",
"middle": [],
"last": "Gupta",
"suffix": ""
},
{
"first": ".",
"middle": [
"."
],
"last": "",
"suffix": ""
}
],
"year": null,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
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"links": null
},
"BIBREF3": {
"ref_id": "b3",
"title": "Which Matters Most? Comparing the Impact of Concept and Document Relationships in Topic Models Silvia Terragni",
"authors": [
{
"first": "Debora",
"middle": [],
"last": "Nozza",
"suffix": ""
},
{
"first": "Elisabetta",
"middle": [],
"last": "Fersini",
"suffix": ""
},
{
"first": ".",
"middle": [
"."
],
"last": "Messina Enza",
"suffix": ""
}
],
"year": null,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Which Matters Most? Comparing the Impact of Concept and Document Relationships in Topic Models Silvia Terragni, Debora Nozza, Elisabetta Fersini and Messina Enza . . . . . . . . . . . . . . . . . . . . . . . . . 32",
"links": null
},
"BIBREF4": {
"ref_id": "b4",
"title": "On Task-Level Dialogue Composition of Generative Transformer Model Prasanna Parthasarathi, Sharan Narang and",
"authors": [],
"year": null,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "On Task-Level Dialogue Composition of Generative Transformer Model Prasanna Parthasarathi, Sharan Narang and Arvind Neelakantan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41",
"links": null
},
"BIBREF5": {
"ref_id": "b5",
"title": "How Effectively Can Machines Defend Against Machine-Generated Fake News? An Empirical Study Meghana Moorthy Bhat and Srinivasan Parthasarathy",
"authors": [],
"year": null,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
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"raw_text": "How Effectively Can Machines Defend Against Machine-Generated Fake News? An Empirical Study Meghana Moorthy Bhat and Srinivasan Parthasarathy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47",
"links": null
},
"BIBREF6": {
"ref_id": "b6",
"title": "Label Propagation-Based Semi-Supervised Learning for Hate Speech Classification Ashwin Geet D",
"authors": [
{
"first": "'",
"middle": [],
"last": "Sa",
"suffix": ""
},
{
"first": "Irina",
"middle": [],
"last": "Illina",
"suffix": ""
},
{
"first": "Dominique",
"middle": [],
"last": "Fohr",
"suffix": ""
},
{
"first": "Dietrich",
"middle": [],
"last": "Klakow",
"suffix": ""
},
{
"first": "Dana",
"middle": [
". . . . ."
],
"last": "Ruiter",
"suffix": ""
}
],
"year": null,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Label Propagation-Based Semi-Supervised Learning for Hate Speech Classification Ashwin Geet D'Sa, Irina Illina, Dominique Fohr, Dietrich Klakow and Dana Ruiter . . . . . . . . . . . 53",
"links": null
},
"BIBREF7": {
"ref_id": "b7",
"title": "Layout-Aware Text Representations Harm Clustering Documents by Type Catherine Finegan-Dollak and",
"authors": [],
"year": null,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Layout-Aware Text Representations Harm Clustering Documents by Type Catherine Finegan-Dollak and Ashish Verma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59",
"links": null
},
"BIBREF8": {
"ref_id": "b8",
"title": "An Analysis of Capsule Networks for Part of Speech Tagging in High-and Low-resource Scenarios Andrew Zupon",
"authors": [],
"year": null,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "An Analysis of Capsule Networks for Part of Speech Tagging in High-and Low-resource Scenarios Andrew Zupon, Faiz Rafique and Mihai Surdeanu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65",
"links": null
},
"BIBREF9": {
"ref_id": "b9",
"title": "Can Knowledge Graph Embeddings Tell Us What Fact-checked Claims Are About? Valentina Beretta",
"authors": [],
"year": null,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Can Knowledge Graph Embeddings Tell Us What Fact-checked Claims Are About? Valentina Beretta, S\u00e9bastien Harispe, Katarina Boland, Luke Lo Seen, Konstantin Todorov and",
"links": null
},
"BIBREF11": {
"ref_id": "b11",
"title": "Do Transformers Dream of Inference, or Can Pretrained Generative Models Learn Implicit Inferential Rules? Zhengzhong Liang and",
"authors": [],
"year": null,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Do Transformers Dream of Inference, or Can Pretrained Generative Models Learn Implicit Inferential Rules? Zhengzhong Liang and Mihai Surdeanu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75",
"links": null
},
"BIBREF12": {
"ref_id": "b12",
"title": "Counterfactually-Augmented SNLI Training Data Does Not Yield Better Generalization Than Unaugmented Data William Huang",
"authors": [
{
"first": "Haokun",
"middle": [],
"last": "Liu",
"suffix": ""
},
{
"first": "Samuel",
"middle": [
"R"
],
"last": "Bowman",
"suffix": ""
},
{
"first": ".",
"middle": [
"."
],
"last": "",
"suffix": ""
}
],
"year": null,
"venue": "",
"volume": "87",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Counterfactually-Augmented SNLI Training Data Does Not Yield Better Generalization Than Unaug- mented Data William Huang, Haokun Liu and Samuel R. Bowman . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 NMF Ensembles? Not for Text Summarization! Alka Khurana and Vasudha Bhatnagar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87",
"links": null
},
"BIBREF13": {
"ref_id": "b13",
"title": "93 HINT3: Raising the bar for Intent Detection in the Wild Gaurav Arora",
"authors": [],
"year": null,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "If You Build Your Own NER Scorer, Non-replicable Results Will Come Constantine Lignos and Marjan Kamyab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 HINT3: Raising the bar for Intent Detection in the Wild Gaurav Arora, Chirag Jain, Manas Chaturvedi and Krupal Modi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99",
"links": null
},
"BIBREF16": {
"ref_id": "b16",
"title": "15 Thematic session: representation learning 8:15-8:45 Thematic session: dialogue",
"authors": [
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"raw_text": "Program Thursday, November 19, 2020 7:00-7:15 Opening remarks 7:15-8:15 Invited talk: Rada Mihalcea (University of Michigan) The ups and downs of word embeddings 8:45-9:15 Thematic session: representation learning 8:15-8:45 Thematic session: dialogue 9:15-10:00 Social break / meal time 10:00-11:00 Invited talk: Byron C. Wallace (Northeastern University) Negative results yield interesting questions, or: a bunch of stuff that didn't work 11:00-11:30 Thematic session: question answering 11:30-12:00 Thematic session: natural language inference 12:00-12:30 Thematic session: lessons learned the hard way 12:30-13:00 Social break / meal time 13:00-14:00 Interactive orals 14:00-14:45 Panel discussion 14:45-15:00 Breakout 15:00-15:15 Closing remarks 15:15-16",
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