| 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 | 2561 | 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 | 1297 | 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 | 840 | 2019 |
| 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 | 435 | 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 | 366 | 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 | 346 | 2021 |
| 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 | 297 | 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 | 277 | 2017 |
| 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 | 266 | 2021 |
| Understanding Dataset Difficulty with V-Usable Information. K Ethayarajh, Y Choi, S Swayamdipta International Conference on Machine Learning, 5988-6008, 2022 | 252 | 2022 |
| Adversarial Filters of Dataset Biases R Le Bras, S Swayamdipta, C Bhagavatula, R Zellers, M Peters, ... Proceedings of ICML, 2019 | 241 | 2019 |
| 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 | 226* | 2020 |
| WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation A Liu, S Swayamdipta, NA Smith, Y Choi Findings of the Association for Computational Linguistics: EMNLP 2022, 6826–6847, 2022 | 222 | 2022 |
| 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 | 174 | 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 | 174 | 2020 |
| 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 | 153 | 2017 |
| Reframing Human-AI Collaboration for Generating Free-Text Explanations S Wiegreffe, J Hessel, S Swayamdipta, M Riedl, Y Choi Proceedings of the 2022 Conference of the North American Chapter of the …, 2021 | 150 | 2021 |
| 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 | 118 | 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 | 116 | 2021 |
| We're afraid language models aren't modeling ambiguity A Liu, Z Wu, J Michael, A Suhr, P West, A Koller, S Swayamdipta, ... arXiv preprint arXiv:2304.14399, 2023 | 82 | 2023 |
Noah A. SmithUniversity of Washington; Allen Institute for Artificial IntelligenceEmail confirmado em cs.washington.edu