| BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding J Devlin, MW Chang, K Lee, K Toutanova arXiv preprint arXiv:1810.04805, 2018 | 12886 | 2018 |
| Deep contextualized word representations ME Peters, M Neumann, M Iyyer, M Gardner, C Clark, K Lee, ... arXiv preprint arXiv:1802.05365, 2018 | 5229 | 2018 |
| Legibility and predictability of robot motion AD Dragan, KCT Lee, SS Srinivasa 2013 8th ACM/IEEE International Conference on Human-Robot Interaction (HRI …, 2013 | 401 | 2013 |
| End-to-end Neural Coreference Resolution K Lee, L He, M Lewis, L Zettlemoyer EMNLP, 2017 | 389 | 2017 |
| Deep semantic role labeling: What works and what’s next L He, K Lee, M Lewis, L Zettlemoyer Proceedings of the 55th Annual Meeting of the Association for Computational …, 2017 | 312 | 2017 |
| Natural questions: a benchmark for question answering research T Kwiatkowski, J Palomaki, O Redfield, M Collins, A Parikh, C Alberti, ... Transactions of the Association for Computational Linguistics 7, 453-466, 2019 | 261 | 2019 |
| Higher-order Coreference Resolution with Coarse-to-fine Inference K Lee, L He, L Zettlemoyer arXiv preprint arXiv:1804.05392, 2018 | 145 | 2018 |
| Broad-coverage CCG Semantic Parsing with AMR Y Artzi, K Lee, L Zettlemoyer Proceedings of the 2015 Conference on Empirical Methods in Natural Language …, 2015 | 138 | 2015 |
| Jointly predicting predicates and arguments in neural semantic role labeling L He, K Lee, O Levy, L Zettlemoyer arXiv preprint arXiv:1805.04787, 2018 | 109 | 2018 |
| Learning Recurrent Span Representations for Extractive Question Answering K Lee, S Salant, T Kwiatkowski, A Parikh, D Das, J Berant arXiv preprint arXiv:1611.01436, 2016 | 108 | 2016 |
| Latent Retrieval for Weakly Supervised Open Domain Question Answering K Lee, MW Chang, K Toutanova arXiv preprint arXiv:1906.00300, 2019 | 105 | 2019 |
| Lstm ccg parsing M Lewis, K Lee, L Zettlemoyer Proceedings of the 2016 Conference of the North American Chapter of the …, 2016 | 85 | 2016 |
| REALM: Retrieval-Augmented Language Model Pre-Training K Guu, K Lee, Z Tung, P Pasupat, MW Chang arXiv preprint arXiv:2002.08909, 2020 | 76 | 2020 |
| Well-read students learn better: On the importance of pre-training compact models I Turc, MW Chang, K Lee, K Toutanova arXiv preprint arXiv:1908.08962, 2019 | 72* | 2019 |
| Context-dependent semantic parsing for time expressions K Lee, Y Artzi, J Dodge, L Zettlemoyer Proceedings of the Conference of the Association for Computational …, 2014 | 71 | 2014 |
| BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions C Clark, K Lee, MW Chang, T Kwiatkowski, M Collins, K Toutanova arXiv preprint arXiv:1905.10044, 2019 | 63 | 2019 |
| Sorry Dave, I'm Afraid I Can't Do That: Explaining Unachievable Robot Tasks Using Natural Language. V Raman, C Lignos, C Finucane, KCT Lee, MP Marcus, H Kress-Gazit Robotics: Science and Systems 2 (1), 2.1, 2013 | 57 | 2013 |
| Syntactic Scaffolds for Semantic Structures S Swayamdipta, S Thomson, K Lee, L Zettlemoyer, C Dyer, NA Smith arXiv preprint arXiv:1808.10485, 2018 | 53 | 2018 |
| A BERT Baseline for the Natural Questions C Alberti, K Lee, M Collins arXiv preprint arXiv:1901.08634, 2019 | 48 | 2019 |
| Event detection and factuality assessment with non-expert supervision K Lee, Y Artzi, Y Choi, L Zettlemoyer Proceedings of the 2015 Conference on Empirical Methods in Natural Language …, 2015 | 44 | 2015 |
Ming-Wei ChangGoogle ResearchVerified email at google.com
