| 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 | 2790* | 2019 |
| Neural word embedding as implicit matrix factorization O Levy, Y Goldberg Advances in neural information processing systems 27, 2177-2185, 2014 | 1708 | 2014 |
| GLUE: A multi-task benchmark and analysis platform for natural language understanding A Wang, A Singh, J Michael, F Hill, O Levy, SR Bowman arXiv preprint arXiv:1804.07461, 2018 | 1389 | 2018 |
| word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method Y Goldberg, O Levy arXiv preprint arXiv:1402.3722, 2014 | 1363 | 2014 |
| Improving distributional similarity with lessons learned from word embeddings O Levy, Y Goldberg, I Dagan Transactions of the Association for Computational Linguistics 3, 211-225, 2015 | 1298 | 2015 |
| Dependency-Based Word Embeddings O Levy, Y Goldberg Proceedings of the 52nd Annual Meeting of the Association for Computational …, 2014 | 1184 | 2014 |
| Linguistic Regularities in Sparse and Explicit Word Representations O Levy, Y Goldberg Proceedings of the Eighteenth Conference on Computational Natural Language …, 2014 | 587 | 2014 |
| 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 | 546 | 2019 |
| What does bert look at? an analysis of bert's attention K Clark, U Khandelwal, O Levy, CD Manning arXiv preprint arXiv:1906.04341, 2019 | 403 | 2019 |
| Annotation artifacts in natural language inference data S Gururangan, S Swayamdipta, O Levy, R Schwartz, SR Bowman, ... arXiv preprint arXiv:1803.02324, 2018 | 389 | 2018 |
| 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 | 363 | 2020 |
| Superglue: A stickier benchmark for general-purpose language understanding systems A Wang, Y Pruksachatkun, N Nangia, A Singh, J Michael, F Hill, O Levy, ... arXiv preprint arXiv:1905.00537, 2019 | 311 | 2019 |
| code2vec: Learning distributed representations of code U Alon, M Zilberstein, O Levy, E Yahav Proceedings of the ACM on Programming Languages 3 (POPL), 1-29, 2019 | 298 | 2019 |
| Do Supervised Distributional Methods Really Learn Lexical Inference Relations? O Levy, S Remus, C Biemann, I Dagan Proceedings of the 2015 Conference of the North American Chapter of the …, 2015 | 197 | 2015 |
| code2seq: Generating sequences from structured representations of code U Alon, S Brody, O Levy, E Yahav arXiv preprint arXiv:1808.01400, 2018 | 186 | 2018 |
| Are sixteen heads really better than one? P Michel, O Levy, G Neubig arXiv preprint arXiv:1905.10650, 2019 | 182 | 2019 |
| Zero-shot relation extraction via reading comprehension O Levy, M Seo, E Choi, L Zettlemoyer arXiv preprint arXiv:1706.04115, 2017 | 159 | 2017 |
| Jointly predicting predicates and arguments in neural semantic role labeling L He, K Lee, O Levy, L Zettlemoyer arXiv preprint arXiv:1805.04787, 2018 | 123 | 2018 |
| Mask-predict: Parallel decoding of conditional masked language models M Ghazvininejad, O Levy, Y Liu, L Zettlemoyer arXiv preprint arXiv:1904.09324, 2019 | 118* | 2019 |
| A Simple Word Embedding Model for Lexical Substitution O Melamud, O Levy, I Dagan Proceedings of the 1st Workshop on Vector Space Modeling for Natural …, 2015 | 102 | 2015 |
Luke ZettlemoyerUniversity of Washington; FacebookVerified email at cs.washington.edu
