| BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding J Devlin, MW Chang, K Lee, K Toutanova https://arxiv.org/abs/1810.04805, 2018 | 105955 | 2018 |
| 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 | 2378 | 2019 |
| REALM: Retrieval-Augmented Language Model Pre-Training K Guu, K Lee, Z Tung, P Pasupat, MW Chang | 1559* | 2020 |
| Load forecasting using support vector machines: A study on EUNITE competition 2001 BJ Chen, MW Chang, CJ Lin Power Systems, IEEE Transactions on 19 (4), 1821-1830, 2004 | 1140 | 2004 |
| Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, ... arXiv preprint arXiv:2312.11805, 2023 | 963 | 2023 |
| Latent retrieval for weakly supervised open domain question answering K Lee, MW Chang, K Toutanova arXiv preprint arXiv:1906.00300, 2019 | 929 | 2019 |
| 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 | 895 | 2019 |
| 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 | 864* | 2019 |
| Semantic Parsing via Staged Query Graph Generation: Question Answering with Knowledge Base W Yih, MW Chang, X He, J Gao ACL, 2015 | 844 | 2015 |
| A knowledge-grounded neural conversation model M Ghazvininejad, C Brockett, MW Chang, B Dolan, J Gao, W Yih, ... Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018 | 641 | 2018 |
| The value of semantic parse labeling for knowledge base question answering W Yih, M Richardson, C Meek, MW Chang, J Suh Proceedings of the 54th Annual Meeting of the Association for Computational …, 2016 | 457 | 2016 |
| Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv J Devlin, MW Chang, K Lee, K Toutanova arXiv preprint arXiv:1810.04805, 2019 | 452 | 2019 |
| Importance of Semantic Representation: Dataless Classification. MW Chang, LA Ratinov, D Roth, V Srikumar Aaai 2, 830-835, 2008 | 350 | 2008 |
| Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) J Devlin, MW Chang, K Lee, K Toutanova Association for Computational Linguistics, 4171-4186, 2019 | 321 | 2019 |
| Question answering using enhanced lexical semantic models SW Yih, MW Chang, C Meek, A Pastusiak Proceedings of the 51st Annual Meeting of the Association for Computational …, 2013 | 280 | 2013 |
| Driving semantic parsing from the world’s response J Clarke, D Goldwasser, MW Chang, D Roth Proceedings of the fourteenth conference on computational natural language …, 2010 | 278 | 2010 |
| Zero-shot entity linking by reading entity descriptions L Logeswaran, MW Chang, K Lee, K Toutanova, J Devlin, H Lee arXiv preprint arXiv:1906.07348, 2019 | 270 | 2019 |
| Guiding semi-supervision with constraint-driven learning MW Chang, L Ratinov, D Roth Proceedings of the 45th annual meeting of the association of computational …, 2007 | 261 | 2007 |
| Large dual encoders are generalizable retrievers J Ni, C Qu, J Lu, Z Dai, GH Ábrego, J Ma, VY Zhao, Y Luan, KB Hall, ... arXiv preprint arXiv:2112.07899, 2021 | 260 | 2021 |
| To link or not to link? a study on end-to-end tweet entity linking S Guo, MW Chang, E Kiciman Proceedings of the 2013 conference of the North American chapter of the …, 2013 | 256 | 2013 |
Kenton LeeGoogle DeepMind在 google.com 的电子邮件经过验证
