| Don't Stop Pretraining: Adapt Language Models to Domains and Tasks S Gururangan, A Marasović, S Swayamdipta, K Lo, I Beltagy, D Downey, ... Proceedings of the 58th Annual Meeting of the Association for Computational …, 2020 | 1214 | 2020 |
| Annotation artifacts in natural language inference data S Gururangan, S Swayamdipta, O Levy, R Schwartz, SR Bowman, ... Proceedings of the 2016 Conference of the North American Chapter of the …, 2018 | 875 | 2018 |
| Transfer learning in natural language processing S Ruder, ME Peters, S Swayamdipta, T Wolf Proceedings of the 2019 conference of the North American chapter of the …, 2019 | 414 | 2019 |
| A dependency parser for tweets L Kong, N Schneider, S Swayamdipta, A Bhatia, C Dyer, NA Smith Proceedings of the 2014 Conference on Empirical Methods in Natural Language …, 2014 | 280 | 2014 |
| Dynet: The dynamic neural network toolkit G Neubig, C Dyer, Y Goldberg, A Matthews, W Ammar, A Anastasopoulos, ... arXiv preprint arXiv:1701.03980, 2017 | 253 | 2017 |
| Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics S Swayamdipta, R Schwartz, N Lourie, Y Wang, H Hajishirzi, NA Smith, ... Proceedings of the 2020 Conference on Empirical Methods in Natural Language …, 2020 | 170 | 2020 |
| Adversarial Filters of Dataset Biases R Le Bras, S Swayamdipta, C Bhagavatula, R Zellers, M Peters, ... Proceedings of ICML, 2019 | 147 | 2019 |
| Frame-semantic parsing with softmax-margin segmental rnns and a syntactic scaffold S Swayamdipta, S Thomson, C Dyer, NA Smith arXiv preprint arXiv:1706.09528, 2017 | 122 | 2017 |
| Syntactic Scaffolds for Semantic Structures S Swayamdipta, S Thomson, K Lee, L Zettlemoyer, C Dyer, NA Smith Proceedings of the 2018 Conference on Empirical Methods in Natural Language …, 2018 | 104 | 2018 |
| G-Daug: Generative Data Augmentation for Commonsense Reasoning Y Yang, C Malaviya, J Fernandez, S Swayamdipta, RL Bras, JP Wang, ... Findings of the Association for Computational Linguistics: EMNLP 2020, 1008 …, 2020 | 100* | 2020 |
| DExperts: Decoding-time controlled text generation with experts and anti-experts A Liu, M Sap, X Lu, S Swayamdipta, C Bhagavatula, NA Smith, Y Choi Proceedings of the 59th Annual Meeting of the Association for Computational …, 2021 | 87 | 2021 |
| The Right Tool for the Job: Matching Model and Instance Complexities R Schwartz, G Stanovsky, S Swayamdipta, J Dodge, NA Smith Proceedings of the 58th Annual Meeting of the Association for Computational …, 2020 | 82 | 2020 |
| Challenges in Automated Debiasing for Toxic Language Detection X Zhou, M Sap, S Swayamdipta, NA Smith, Y Choi Proceedings of 16th Conference of the European Chapter of the Association …, 2021 | 77 | 2021 |
| Mauve: Measuring the gap between neural text and human text using divergence frontiers K Pillutla, S Swayamdipta, R Zellers, J Thickstun, S Welleck, Y Choi, ... Advances in Neural Information Processing Systems 34, 4816-4828, 2021 | 74 | 2021 |
| Greedy, joint syntactic-semantic parsing with stack LSTMs S Swayamdipta, M Ballesteros, C Dyer, NA Smith SIGNLL Conference on Computational Natural Language Learning 16, http …, 2016 | 62 | 2016 |
| Learning joint semantic parsers from disjoint data H Peng, S Thomson, S Swayamdipta, NA Smith Proceedings of the 2018 Conference of the North American Chapter of the …, 2018 | 58 | 2018 |
| Annotators with attitudes: How annotator beliefs and identities bias toxic language detection M Sap, S Swayamdipta, L Vianna, X Zhou, Y Choi, NA Smith Proceedings of the 2022 Conference of the North American Chapter of the …, 2021 | 54 | 2021 |
| WANLI: Worker and ai collaboration for natural language inference dataset creation A Liu, S Swayamdipta, NA Smith, Y Choi arXiv preprint arXiv:2201.05955, 2022 | 46 | 2022 |
| Polyglot Semantic Role Labeling P Mulcaire, S Swayamdipta, N Smith Proceedings of the 56th Annual Meeting of the Association for Computational …, 2018 | 42 | 2018 |
| Contrastive explanations for model interpretability A Jacovi, S Swayamdipta, S Ravfogel, Y Elazar, Y Choi, Y Goldberg Proceedings of the 2021 Conference on Empirical Methods in Natural Language …, 2021 | 36 | 2021 |
Noah A. SmithUniversity of Washington; Allen Institute for Artificial IntelligenceVerified email at cs.washington.edu
