| A Dependency Parser for Tweets L Kong, N Schneider, S Swayamdipta, A Bhatia, C Dyer, NA Smith EMNLP 2014, 2014 | 286 | 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 | 261 | 2017 |
| Random feature attention H Peng, N Pappas, D Yogatama, R Schwartz, NA Smith, L Kong arXiv preprint arXiv:2103.02143, 2021 | 206 | 2021 |
| What do recurrent neural network grammars learn about syntax? A Kuncoro, M Ballesteros, L Kong, C Dyer, G Neubig, NA Smith arXiv preprint arXiv:1611.05774, 2016 | 140 | 2016 |
| Episodic memory in lifelong language learning C de Masson D'Autume, S Ruder, L Kong, D Yogatama Advances in Neural Information Processing Systems 32, 2019 | 137 | 2019 |
| Segmental recurrent neural networks L Kong, C Dyer, NA Smith arXiv preprint arXiv:1511.06018, 2015 | 129 | 2015 |
| Unifiedskg: Unifying and multi-tasking structured knowledge grounding with text-to-text language models T Xie, CH Wu, P Shi, R Zhong, T Scholak, M Yasunaga, CS Wu, M Zhong, ... arXiv preprint arXiv:2201.05966, 2022 | 89 | 2022 |
| cosformer: Rethinking softmax in attention Z Qin, W Sun, H Deng, D Li, Y Wei, B Lv, J Yan, L Kong, Y Zhong arXiv preprint arXiv:2202.08791, 2022 | 87 | 2022 |
| Distilling an ensemble of greedy dependency parsers into one MST parser A Kuncoro, M Ballesteros, L Kong, C Dyer, NA Smith arXiv preprint arXiv:1609.07561, 2016 | 80 | 2016 |
| Diffuseq: Sequence to sequence text generation with diffusion models S Gong, M Li, J Feng, Z Wu, LP Kong arXiv preprint arXiv:2210.08933, 2022 | 74 | 2022 |
| Segmental recurrent neural networks for end-to-end speech recognition L Lu, L Kong, C Dyer, NA Smith, S Renals arXiv preprint arXiv:1603.00223, 2016 | 73 | 2016 |
| Adaptive semiparametric language models D Yogatama, C de Masson d’Autume, L Kong Transactions of the Association for Computational Linguistics 9, 362-373, 2021 | 71 | 2021 |
| Learning and evaluating general linguistic intelligence D Yogatama, CM d'Autume, J Connor, T Kocisky, M Chrzanowski, L Kong, ... arXiv preprint arXiv:1901.11373, 2019 | 63 | 2019 |
| A contrastive framework for neural text generation Y Su, T Lan, Y Wang, D Yogatama, L Kong, N Collier Advances in Neural Information Processing Systems 35, 21548-21561, 2022 | 58 | 2022 |
| Document context language models Y Ji, T Cohn, L Kong, C Dyer, J Eisenstein arXiv preprint arXiv:1511.03962, 2015 | 41 | 2015 |
| Bayesian Optimization of Text Representations D Yogatama, L Kong, NA Smith Proceedings of the Conference on Empirical Methods in Natural Language …, 2015 | 41 | 2015 |
| Cyprien de Masson d’Autume, Lei Yu, Wang Ling, Zihang Dai, and Dani Yogatama. 2020. A mutual information maximization perspective of language representation learning L Kong 8th International Conference on Learning Representations, ICLR, 26-30, 2020 | 39 | 2020 |
| Zerogen: Efficient zero-shot learning via dataset generation J Ye, J Gao, Q Li, H Xu, J Feng, Z Wu, T Yu, L Kong arXiv preprint arXiv:2202.07922, 2022 | 38 | 2022 |
| End-to-end neural segmental models for speech recognition H Tang, L Lu, L Kong, K Gimpel, K Livescu, C Dyer, NA Smith, S Renals IEEE Journal of Selected Topics in Signal Processing 11 (8), 1254-1264, 2017 | 37 | 2017 |
| SyntaxNet models for the CoNLL 2017 shared task C Alberti, D Andor, I Bogatyy, M Collins, D Gillick, L Kong, T Koo, J Ma, ... arXiv preprint arXiv:1703.04929, 2017 | 36 | 2017 |
Chris DyerDeepMind, Carnegie MellonVerified email at google.com
