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| "text": "Publication of negative results is difficult in most fields, and the current focus on benchmark-driven 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. 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. However, the tide may be turning. Despite the pandemic, the third iteration of the Workshop on Insights from Negative Results attracted 43 submissions and 1 from ACL Rolling Reviews. The workshop maintained roughly the same focus, welcoming 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", | |
| "sec_num": null | |
| }, | |
| { | |
| "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": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "\u2022 ablation studies of components in previously proposed models, showing that their contributions are different from what was initially reported;", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "\u2022 datasets or probing tasks showing that previous approaches do not generalize to other domains or language phenomena;", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "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": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "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": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "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;", | |
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| "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/themes, 16 papers from our accepted proceedings discussed \"lessons learned in pretraining/training neural architectures/large language models\"; 10 discussed \"great ideas that didn't work\"; 10 papers performed probing tasks and datasets to draw deeper insights or understand reasons for success/failure; 9 dealt with issues of robustness, generalizability, compositionality, and few-shot performance; 2 were on the topic of \"analyzing biases, errors, spurious correlations in data/model\"; 1 paper focused on issues in replication of research results and 1 paper on the impact of data augmentation. Some submissions fit in more than one category. We accepted 24 short papers (55.8% acceptance rate) and one paper from ACL Rolling Reviews. We hope the workshop will continue to contribute to the many reality-check discussions on progress in NLP. Bio: Barbara Plank is Chair (Professor) of AI and Computational Linguistics at LMU Munich, with a part-time affiliation at the IT University of Copenhagen. Her research focuses on various aspects of NLP and include learning under sample selection bias (domain adaptation, transfer learning), annotation bias (human disagreements and human uncertainty), learning from beyond the text, and in general learning under limited supervision. Barbara is the recipient of a 2019 Sapere Aude Research Leader grant and an Amazon Research Award. Barbara is on the advisory board of the European Association for Computational Linguistics, publicity director of the Association for Computational Linguistics and since 2022 president of the Northern European Association for Language Technology.", | |
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| "sec_num": null | |
| }, | |
| { | |
| "text": "https://mirror.aclweb.org/acl2010/papers.html 2 http://jinr.site.uottawa.ca/", | |
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| "BIBREF1": { | |
| "ref_id": "b1", | |
| "title": "Do Dependency Relations Help in the Task of Stance Detection? Alessandra Teresa Cignarella", | |
| "authors": [ | |
| { | |
| "first": "Cristina", | |
| "middle": [], | |
| "last": "Bosco", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Paolo", | |
| "middle": [], | |
| "last": "Rosso", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": ".", | |
| "middle": [ | |
| "." | |
| ], | |
| "last": "", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": null, | |
| "venue": "", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Do Dependency Relations Help in the Task of Stance Detection? Alessandra Teresa Cignarella, Cristina Bosco and Paolo Rosso . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10", | |
| "links": null | |
| }, | |
| "BIBREF2": { | |
| "ref_id": "b2", | |
| "title": "Evaluating the Practical Utility of Confidence-score based Techniques for Unsupervised Open-world Classification Sopan Khosla and Rashmi Gangadharaiah", | |
| "authors": [], | |
| "year": null, | |
| "venue": "", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Evaluating the Practical Utility of Confidence-score based Techniques for Unsupervised Open-world Classification Sopan Khosla and Rashmi Gangadharaiah . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18", | |
| "links": null | |
| }, | |
| "BIBREF3": { | |
| "ref_id": "b3", | |
| "title": "Extending the Scope of Out-of-Domain: Examining QA models in multiple subdomains Chenyang Lyu", | |
| "authors": [ | |
| { | |
| "first": "Jennifer", | |
| "middle": [], | |
| "last": "Foster", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Yvette", | |
| "middle": [], | |
| "last": "Graham", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": ".", | |
| "middle": [ | |
| "." | |
| ], | |
| "last": "", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": null, | |
| "venue": "", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Extending the Scope of Out-of-Domain: Examining QA models in multiple subdomains Chenyang Lyu, Jennifer Foster and Yvette Graham . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24", | |
| "links": null | |
| }, | |
| "BIBREF4": { | |
| "ref_id": "b4", | |
| "title": "What Do You Get When You Cross Beam Search with Nucleus Sampling? Uri Shaham and", | |
| "authors": [], | |
| "year": null, | |
| "venue": "", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "What Do You Get When You Cross Beam Search with Nucleus Sampling? Uri Shaham and Omer Levy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38", | |
| "links": null | |
| }, | |
| "BIBREF5": { | |
| "ref_id": "b5", | |
| "title": "How Much Do Modifications to Transformer Language Models Affect Their Ability to Learn Linguistic Knowledge? Simeng Sun", | |
| "authors": [], | |
| "year": null, | |
| "venue": "", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "How Much Do Modifications to Transformer Language Models Affect Their Ability to Learn Linguistic Knowledge? Simeng Sun, Brian Dillon and Mohit Iyyer. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .46", | |
| "links": null | |
| }, | |
| "BIBREF6": { | |
| "ref_id": "b6", | |
| "title": "Inflection as a Data Augmentation Method for Parsing Alberto Mu\u00f1oz-Ortiz, Carlos G\u00f3mez-Rodr\u00edguez and David Vilares", | |
| "authors": [ | |
| { | |
| "first": "", | |
| "middle": [], | |
| "last": "Cross", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": null, | |
| "venue": "", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Cross-lingual Inflection as a Data Augmentation Method for Parsing Alberto Mu\u00f1oz-Ortiz, Carlos G\u00f3mez-Rodr\u00edguez and David Vilares . . . . . . . . . . . . . . . . . . . . . . . . . 54", | |
| "links": null | |
| }, | |
| "BIBREF7": { | |
| "ref_id": "b7", | |
| "title": "Is BERT Robust to Label Noise? A Study on Learning with Noisy Labels", | |
| "authors": [], | |
| "year": null, | |
| "venue": "", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Is BERT Robust to Label Noise? A Study on Learning with Noisy Labels in Text Classification Dawei Zhu, Michael A. Hedderich, Fangzhou Zhai, David Ifeoluwa Adelani and Dietrich Klakow 62", | |
| "links": null | |
| }, | |
| "BIBREF8": { | |
| "ref_id": "b8", | |
| "title": "Ancestor-to-Creole Transfer is Not a Walk in the Park Heather Lent", | |
| "authors": [], | |
| "year": null, | |
| "venue": "", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Ancestor-to-Creole Transfer is Not a Walk in the Park Heather Lent, Emanuele Bugliarello and Anders S\u00f8gaard . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68", | |
| "links": null | |
| }, | |
| "BIBREF10": { | |
| "ref_id": "b10", | |
| "title": "Evaluating Biomedical Word Embeddings for Vocabulary Alignment at Scale in the UMLS Metathesaurus Using Siamese Networks Goonmeet Bajaj", | |
| "authors": [], | |
| "year": null, | |
| "venue": "", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Evaluating Biomedical Word Embeddings for Vocabulary Alignment at Scale in the UMLS Metathesau- rus Using Siamese Networks Goonmeet Bajaj, Vinh Nguyen, Thilini Wijesiriwardene, Hong Yung Yip, Vishesh Javangula,", | |
| "links": null | |
| }, | |
| "BIBREF12": { | |
| "ref_id": "b12", | |
| "title": "On the Impact of Data Augmentation on Downstream Performance in Natural Language Processing Itsuki Okimura", | |
| "authors": [ | |
| { | |
| "first": "Machel", | |
| "middle": [], | |
| "last": "Reid", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Makoto", | |
| "middle": [], | |
| "last": "Kawano", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Yutaka", | |
| "middle": [], | |
| "last": "Matsuo", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": ".", | |
| "middle": [ | |
| "." | |
| ], | |
| "last": "", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": null, | |
| "venue": "", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "On the Impact of Data Augmentation on Downstream Performance in Natural Language Processing Itsuki Okimura, Machel Reid, Makoto Kawano and Yutaka Matsuo . . . . . . . . . . . . . . . . . . . . . . . . 88", | |
| "links": null | |
| }, | |
| "BIBREF13": { | |
| "ref_id": "b13", | |
| "title": "Can Question Rewriting Help Conversational Question Answering?", | |
| "authors": [ | |
| { | |
| "first": "Etsuko", | |
| "middle": [], | |
| "last": "Ishii", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Yan", | |
| "middle": [], | |
| "last": "Xu", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Samuel", | |
| "middle": [], | |
| "last": "Cahyawijaya", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": ".", | |
| "middle": [ | |
| "." | |
| ], | |
| "last": "Bryan Wilie", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": null, | |
| "venue": "", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Can Question Rewriting Help Conversational Question Answering? Etsuko Ishii, Yan Xu, Samuel Cahyawijaya and Bryan Wilie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94", | |
| "links": null | |
| }, | |
| "BIBREF14": { | |
| "ref_id": "b14", | |
| "title": "Clustering Examples in Multi-Dataset Benchmarks with Item Response Theory Pedro Rodriguez, Phu Mon Htut", | |
| "authors": [ | |
| { | |
| "first": "P", | |
| "middle": [], | |
| "last": "John", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Jo\u00e3o", | |
| "middle": [], | |
| "last": "Lalor", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": ".", | |
| "middle": [ | |
| "." | |
| ], | |
| "last": "Sedoc", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": null, | |
| "venue": "", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Clustering Examples in Multi-Dataset Benchmarks with Item Response Theory Pedro Rodriguez, Phu Mon Htut, John P. Lalor and Jo\u00e3o Sedoc . . . . . . . . . . . . . . . . . . . . . . . . . . . 100", | |
| "links": null | |
| }, | |
| "BIBREF15": { | |
| "ref_id": "b15", | |
| "title": "On the Limits of Evaluating Embodied Agent Model Generalization Using Validation Sets Hyounghun", | |
| "authors": [], | |
| "year": null, | |
| "venue": "", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "On the Limits of Evaluating Embodied Agent Model Generalization Using Validation Sets Hyounghun Kim, Aishwarya Padmakumar, Di Jin, Mohit Bansal and Dilek Hakkani-Tur . . . . 113", | |
| "links": null | |
| }, | |
| "BIBREF16": { | |
| "ref_id": "b16", | |
| "title": "119 x The Document Vectors Using Cosine Similarity Revisited Zhang Bingyu and Nikolay Arefyev", | |
| "authors": [ | |
| { | |
| "first": "Maxim", | |
| "middle": [ | |
| "K" | |
| ], | |
| "last": "Surkov", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "D", | |
| "middle": [], | |
| "last": "Vladislav", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Ivan", | |
| "middle": [ | |
| "P" | |
| ], | |
| "last": "Mosin", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "", | |
| "middle": [ | |
| ". . . . . . . . . . . . . . . . . . . . . ." | |
| ], | |
| "last": "Yamshchikov", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": null, | |
| "venue": "", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
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| "num": null, | |
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| "raw_text": "Do Data-based Curricula Work? Maxim K. Surkov, Vladislav D. Mosin and Ivan P. Yamshchikov . . . . . . . . . . . . . . . . . . . . . . . . . . 119 x The Document Vectors Using Cosine Similarity Revisited Zhang Bingyu and Nikolay Arefyev . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129", | |
| "links": null | |
| }, | |
| "BIBREF17": { | |
| "ref_id": "b17", | |
| "title": "Challenges in including extra-linguistic context in pre-trained language models Ionut Teodor Sorodoc, Laura Aina and", | |
| "authors": [ | |
| { | |
| "first": "Gemma", | |
| "middle": [], | |
| "last": "Boleda", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": ".", | |
| "middle": [ | |
| "." | |
| ], | |
| "last": "", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": null, | |
| "venue": "", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Challenges in including extra-linguistic context in pre-trained language models Ionut Teodor Sorodoc, Laura Aina and Gemma Boleda . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134", | |
| "links": null | |
| }, | |
| "BIBREF18": { | |
| "ref_id": "b18", | |
| "title": "Label Errors in BANKING77", | |
| "authors": [], | |
| "year": null, | |
| "venue": "", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Label Errors in BANKING77", | |
| "links": null | |
| }, | |
| "BIBREF20": { | |
| "ref_id": "b20", | |
| "title": "Pathologies of Pre-trained Language Models in Few-shot Fine-tuning Hanjie Chen, Guoqing Zheng", | |
| "authors": [ | |
| { | |
| "first": "Ahmed", | |
| "middle": [], | |
| "last": "Hassan Awadallah", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Yangfeng", | |
| "middle": [ | |
| ". . . . . . . . . . . . ." | |
| ], | |
| "last": "Ji", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": null, | |
| "venue": "", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Pathologies of Pre-trained Language Models in Few-shot Fine-tuning Hanjie Chen, Guoqing Zheng, Ahmed Hassan Awadallah and Yangfeng Ji . . . . . . . . . . . . . . . . . 144", | |
| "links": null | |
| }, | |
| "BIBREF21": { | |
| "ref_id": "b21", | |
| "title": "An Empirical study to understand the Compositional Prowess of Neural Dialog", | |
| "authors": [], | |
| "year": null, | |
| "venue": "", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "An Empirical study to understand the Compositional Prowess of Neural Dialog Models Vinayshekhar Bannihatti Kumar, Vaibhav Kumar, Mukul Bhutani and Alexander Rudnicky . . 154", | |
| "links": null | |
| }, | |
| "BIBREF22": { | |
| "ref_id": "b22", | |
| "title": "Combining Extraction and Generation for Constructing Belief-Consequence Causal Links Maria Alexeeva", | |
| "authors": [], | |
| "year": null, | |
| "venue": "", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Combining Extraction and Generation for Constructing Belief-Consequence Causal Links Maria Alexeeva, Allegra A. Beal A. Beal and Mihai Surdeanu . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159", | |
| "links": null | |
| }, | |
| "BIBREF23": { | |
| "ref_id": "b23", | |
| "title": "165 BPE beyond Word Boundary: How NOT to use Multi Word Expressions in Neural Machine Translation Dipesh Kumar and", | |
| "authors": [], | |
| "year": null, | |
| "venue": "Replicability under Near-Perfect Conditions -A Case-Study from Automatic", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Replicability under Near-Perfect Conditions -A Case-Study from Automatic Summarization Margot Mieskes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165 BPE beyond Word Boundary: How NOT to use Multi Word Expressions in Neural Machine Translation Dipesh Kumar and Avijit Thawani . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172", | |
| "links": null | |
| }, | |
| "BIBREF24": { | |
| "ref_id": "b24", | |
| "title": "Pre-trained language models evaluating themselves -A comparative study Philipp Koch, Matthias A\u00dfenmacher and Christian Heumann", | |
| "authors": [], | |
| "year": null, | |
| "venue": "", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Pre-trained language models evaluating themselves -A comparative study Philipp Koch, Matthias A\u00dfenmacher and Christian Heumann . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180", | |
| "links": null | |
| }, | |
| "BIBREF25": { | |
| "ref_id": "b25", | |
| "title": "Thematic Session 2: Transformers How Much Do Modifications to Transformer Language Models Affect Their Ability to Learn Linguistic Knowledge? Simeng Sun, Brian Dillon and Mohit Iyyer Pathologies of Pre-trained Language Models in Few-shot Fine-tuning Hanjie Chen, Guoqing Zheng, Ahmed Hassan Awadallah and Yangfeng Ji On Isotropy Calibration of Transformer Models Yue Ding, Karolis Martinkus, Damian Pascual", | |
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| { | |
| "first": "Program", | |
| "middle": [], | |
| "last": "Thursday", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2022, | |
| "venue": "30 Thematic Session 3: Towards Better Data Do Data-based Curricula Work? Maxim K. Surkov, Vladislav D. Mosin and Ivan P. Yamshchikov Clustering Examples in Multi-Dataset Benchmarks with Item Response Theory", | |
| "volume": "11", | |
| "issue": "", | |
| "pages": "0--12", | |
| "other_ids": {}, | |
| "num": null, | |
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| "raw_text": "Program Thursday, May 26, 2022 08:45 -09:00 Opening Remarks 09:00 -10:00 Invited Talk: Barbara Plank 10:30 -11:00 Coffee Break 11:00 -11:30 Thematic Session 1: Linguistically Informed Analysis Do Dependency Relations Help in the Task of Stance Detection? Alessandra Teresa Cignarella, Cristina Bosco and Paolo Rosso BPE beyond Word Boundary: How NOT to use Multi Word Expressions in Neural Machine Translation Dipesh Kumar and Avijit Thawani Challenges in including extra-linguistic context in pre-trained language models Ionut Teodor Sorodoc, Laura Aina and Gemma Boleda 11:30 -12:00 Thematic Session 2: Transformers How Much Do Modifications to Transformer Language Models Affect Their Ability to Learn Linguistic Knowledge? Simeng Sun, Brian Dillon and Mohit Iyyer Pathologies of Pre-trained Language Models in Few-shot Fine-tuning Hanjie Chen, Guoqing Zheng, Ahmed Hassan Awadallah and Yangfeng Ji On Isotropy Calibration of Transformer Models Yue Ding, Karolis Martinkus, Damian Pascual, Simon Clematide and Roger Wattenhofer 12:00 -12:30 Thematic Session 3: Towards Better Data Do Data-based Curricula Work? Maxim K. Surkov, Vladislav D. Mosin and Ivan P. Yamshchikov Clustering Examples in Multi-Dataset Benchmarks with Item Response Theory Pedro Rodriguez, Phu Mon Htut, John P. Lalor and Jo\u00e3o Sedoc", | |
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| "content": "<table><tr><td>Valentin Barriere, Joint Research Center Wasi Uddin Ahmad, Amazon Wazir Ali, ILMA University Karachi Xutan Peng, University of Sheffield Yash Parag Butala, Indian Institute of Technology Kharagpur Yev V Perevodchikov, Amazon Keynote Talk: Power, Uncertainty and the Null Tal Linzen IT University of Copenhagen, Denmark Signal and Insights Barbara Plank Bio: Keynote Talk: Off the Beaten Track: To Turn \"Failures\" into IT University of Copenhagen, Denmark</td></tr><tr><td>Invited Speakers</td></tr><tr><td>Barbara Plank, IT University of Copenhagen</td></tr><tr><td>Tal Linzen, New York University</td></tr></table>", | |
| "text": "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. Tal Linzen is an Assistant Professor of Linguistics and Data Science at New York University and a Research Scientist at Google. Before moving to NYU in 2020, he was a faculty member at Johns Hopkins University, a postdoctoral researcher at the \u00c9cole Normale Sup\u00e9rieure in Paris, and a PhD student at NYU. At NYU, Tal directs the Computational Psycholinguistics Lab, which develops computational models of human language comprehension and acquisition, as well as methods for interpreting and evaluating neural network models for language technologies.", | |
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