| Roberta: A robustly optimized bert pretraining approach Y Liu arXiv preprint arXiv:1907.11692 364, 2019 | 31482* | 2019 |
| Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension M Lewis arXiv preprint arXiv:1910.13461, 2019 | 12092 | 2019 |
| Glue: A multi-task benchmark and analysis platform for natural language understanding A Wang arXiv preprint arXiv:1804.07461, 2018 | 8088 | 2018 |
| Neural word embedding as implicit matrix factorization O Levy, Y Goldberg Advances in neural information processing systems 27, 2014 | 2647 | 2014 |
| Superglue: A stickier benchmark for general-purpose language understanding systems A Wang, Y Pruksachatkun, N Nangia, A Singh, J Michael, F Hill, O Levy, ... Advances in neural information processing systems 32, 2019 | 2470 | 2019 |
| word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method Y Goldberg arXiv preprint arXiv:1402.3722, 2014 | 2462 | 2014 |
| 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 | 2346 | 2020 |
| What Does Bert Look At? An Analysis of Bert’s Attention K Clark arXiv preprint arXiv:1906.04341, 2019 | 1943 | 2019 |
| 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 | 1925 | 2015 |
| Dependency-Based Word Embeddings O Levy, Y Goldberg Proceedings of the 52nd Annual Meeting of the Association for Computational …, 2014 | 1772 | 2014 |
| 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 | 1528 | 2019 |
| Beyond the imitation game: Quantifying and extrapolating the capabilities of language models A Srivastava, A Rastogi, A Rao, AAM Shoeb, A Abid, A Fisch, AR Brown, ... arXiv preprint arXiv:2206.04615, 2022 | 1318 | 2022 |
| Annotation artifacts in natural language inference data S Gururangan, S Swayamdipta, O Levy, R Schwartz, SR Bowman, ... arXiv preprint arXiv:1803.02324, 2018 | 1294 | 2018 |
| Are sixteen heads really better than one? P Michel, O Levy, G Neubig Advances in neural information processing systems 32, 2019 | 1160 | 2019 |
| Lima: Less is more for alignment C Zhou, P Liu, P Xu, S Iyer, J Sun, Y Mao, X Ma, A Efrat, P Yu, L Yu, ... Advances in Neural Information Processing Systems 36, 2024 | 931 | 2024 |
| code2seq: Generating sequences from structured representations of code U Alon, S Brody, O Levy, E Yahav arXiv preprint arXiv:1808.01400, 2018 | 897 | 2018 |
| Generalization through memorization: Nearest neighbor language models U Khandelwal, O Levy, D Jurafsky, L Zettlemoyer, M Lewis arXiv preprint arXiv:1911.00172, 2019 | 853 | 2019 |
| Linguistic Regularities in Sparse and Explicit Word Representations O Levy, Y Goldberg Proceedings of the Eighteenth Conference on Computational Natural Language …, 2014 | 826 | 2014 |
| Transformer feed-forward layers are key-value memories M Geva, R Schuster, J Berant, O Levy arXiv preprint arXiv:2012.14913, 2020 | 649 | 2020 |
| Zero-shot relation extraction via reading comprehension O Levy, M Seo, E Choi, L Zettlemoyer arXiv preprint arXiv:1706.04115, 2017 | 632 | 2017 |
Luke ZettlemoyerUniversity of Washington; Meta確認したメール アドレス: cs.washington.edu