| Deep contextualized word representations ME Peters, M Neumann, M Iyyer, M Gardner, C Clark, K Lee, ... arXiv preprint arXiv:1802.05365, 2018 | 5252 | 2018 |
| Roberta: A robustly optimized bert pretraining approach Y Liu, M Ott, N Goyal, J Du, M Joshi, D Chen, O Levy, M Lewis, ... arXiv preprint arXiv:1907.11692, 2019 | 1844* | 2019 |
| Learning to map sentences to logical form: Structured classification with probabilistic categorial grammars LS Zettlemoyer, M Collins Conference on Uncertainty in Artificial Intelligence (UAI), 2005 | 818* | 2005 |
| Knowledge-based weak supervision for information extraction of overlapping relations R Hoffmann, C Zhang, X Ling, L Zettlemoyer, DS Weld Proceedings of the 49th annual meeting of the association for computational …, 2011 | 805 | 2011 |
| Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension M Joshi, E Choi, DS Weld, L Zettlemoyer arXiv preprint arXiv:1705.03551, 2017 | 487 | 2017 |
| Allennlp: A deep semantic natural language processing platform M Gardner, J Grus, M Neumann, O Tafjord, P Dasigi, N Liu, M Peters, ... arXiv preprint arXiv:1803.07640, 2018 | 483 | 2018 |
| Online learning of relaxed CCG grammars for parsing to logical form L Zettlemoyer, M Collins Proceedings of the 2007 Joint Conference on Empirical Methods in Natural …, 2007 | 414 | 2007 |
| End-to-end neural coreference resolution K Lee, L He, M Lewis, L Zettlemoyer arXiv preprint arXiv:1707.07045, 2017 | 387 | 2017 |
| Weakly supervised learning of semantic parsers for mapping instructions to actions Y Artzi, L Zettlemoyer Transactions of the Association for Computational Linguistics 1, 49-62, 2013 | 369 | 2013 |
| Learning to parse natural language commands to a robot control system C Matuszek, E Herbst, L Zettlemoyer, D Fox Experimental robotics, 403-415, 2013 | 356 | 2013 |
| Open question answering over curated and extracted knowledge bases A Fader, L Zettlemoyer, O Etzioni Proceedings of the 20th ACM SIGKDD international conference on Knowledge …, 2014 | 353 | 2014 |
| Paraphrase-driven learning for open question answering A Fader, L Zettlemoyer, O Etzioni Proceedings of the 51st Annual Meeting of the Association for Computational …, 2013 | 312 | 2013 |
| Inducing probabilistic CCG grammars from logical form with higher-order unification T Kwiatkowksi, L Zettlemoyer, S Goldwater, M Steedman Proceedings of the 2010 conference on empirical methods in natural language …, 2010 | 312 | 2010 |
| 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 | 311 | 2017 |
| Scaling semantic parsers with on-the-fly ontology matching T Kwiatkowski, E Choi, Y Artzi, L Zettlemoyer Proceedings of the 2013 conference on empirical methods in natural language …, 2013 | 310 | 2013 |
| Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension M Lewis, Y Liu, N Goyal, M Ghazvininejad, A Mohamed, O Levy, ... arXiv preprint arXiv:1910.13461, 2019 | 296 | 2019 |
| A joint model of language and perception for grounded attribute learning C Matuszek, N FitzGerald, L Zettlemoyer, L Bo, D Fox arXiv preprint arXiv:1206.6423, 2012 | 282 | 2012 |
| Reinforcement learning for mapping instructions to actions SRK Branavan, H Chen, LS Zettlemoyer, R Barzilay Association for Computational Linguistics, 2009 | 246 | 2009 |
| Unsupervised cross-lingual representation learning at scale A Conneau, K Khandelwal, N Goyal, V Chaudhary, G Wenzek, F Guzmán, ... arXiv preprint arXiv:1911.02116, 2019 | 243 | 2019 |
| Spanbert: Improving pre-training by representing and predicting spans M Joshi, D Chen, Y Liu, DS Weld, L Zettlemoyer, O Levy Transactions of the Association for Computational Linguistics 8, 64-77, 2020 | 230 | 2020 |
Kenton LeeGoogleVerified email at google.com
