| BERT rediscovers the classical NLP pipeline I Tenney arXiv preprint arXiv:1905.05950, 2019 | 1673 | 2019 |
| Bloom: A 176b-parameter open-access multilingual language model T Le Scao, A Fan, C Akiki, E Pavlick, S Ilić, D Hesslow, R Castagné, ... | 1482 | 2023 |
| Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference RT McCoy arXiv preprint arXiv:1902.01007, 2019 | 1226 | 2019 |
| Beyond the imitation game: Quantifying and extrapolating the capabilities of language models A Srivastava, A Rastogi, A Rao, AAM Shoeb, A Abid, A Fisch, AR Brown, ... arXiv preprint arXiv:2206.04615, 2022 | 999 | 2022 |
| What do you learn from context? probing for sentence structure in contextualized word representations I Tenney, P Xia, B Chen, A Wang, A Poliak, RT McCoy, N Kim, ... arXiv preprint arXiv:1905.06316, 2019 | 890 | 2019 |
| Optimizing statistical machine translation for text simplification W Xu, C Napoles, E Pavlick, Q Chen, C Callison-Burch Transactions of the Association for Computational Linguistics 4, 401-415, 2016 | 658 | 2016 |
| Openwebtext corpus A Gokaslan, V Cohen, E Pavlick, S Tellex | 422 | 2019 |
| PPDB 2.0: Better paraphrase ranking, fine-grained entailment relations, word embeddings, and style classification E Pavlick, J Ganitkevitch, P Rastogi, B Van Durme, C Callison-Burch Volume 2: Short Papers, 425, 2015 | 392 | 2015 |
| Do prompt-based models really understand the meaning of their prompts? A Webson, E Pavlick arXiv preprint arXiv:2109.01247, 2021 | 332 | 2021 |
| Inherent disagreements in human textual inferences E Pavlick, T Kwiatkowski Transactions of the Association for Computational Linguistics 7, 677-694, 2019 | 271 | 2019 |
| What happens to BERT embeddings during fine-tuning? A Merchant, E Rahimtoroghi, E Pavlick, I Tenney arXiv preprint arXiv:2004.14448, 2020 | 195 | 2020 |
| An empirical analysis of formality in online communication E Pavlick, J Tetreault Transactions of the association for computational linguistics 4, 61-74, 2016 | 165 | 2016 |
| Collecting diverse natural language inference problems for sentence representation evaluation A Poliak, A Haldar, R Rudinger, JE Hu, E Pavlick, AS White, B Van Durme arXiv preprint arXiv:1804.08207, 2018 | 163 | 2018 |
| Mapping language models to grounded conceptual spaces R Patel, E Pavlick International conference on learning representations, 2022 | 122 | 2022 |
| Simple PPDB: A Paraphrase Database for Simplification E Pavlick, C Callison-Burch | 121 | 2016 |
| Can you tell me how to get past sesame street? sentence-level pretraining beyond language modeling A Wang, J Hula, P Xia, R Pappagari, RT McCoy, R Patel, N Kim, I Tenney, ... arXiv preprint arXiv:1812.10860, 2018 | 120 | 2018 |
| Probing what different NLP tasks teach machines about function word comprehension N Kim, R Patel, A Poliak, A Wang, P Xia, RT McCoy, I Tenney, A Ross, ... arXiv preprint arXiv:1904.11544, 2019 | 104 | 2019 |
| Measuring and reducing gendered correlations in pre-trained models K Webster, X Wang, I Tenney, A Beutel, E Pitler, E Pavlick, J Chen, E Chi, ... arXiv preprint arXiv:2010.06032, 2020 | 103 | 2020 |
| Can language models encode perceptual structure without grounding? a case study in color M Abdou, A Kulmizev, D Hershcovich, S Frank, E Pavlick, A Søgaard arXiv preprint arXiv:2109.06129, 2021 | 96 | 2021 |
| The language demographics of amazon mechanical turk E Pavlick, M Post, A Irvine, D Kachaev, C Callison-Burch Transactions of the Association for Computational Linguistics 2, 79-92, 2014 | 95 | 2014 |
Chris Callison-BurchProfessor, University of PennsylvaniaVerified email at upenn.edu
