QuAC: Question answering in context E Choi, H He, M Iyyer, M Yatskar, W Yih, Y Choi, P Liang, L Zettlemoyer arXiv preprint arXiv:1808.07036, 2018 | 866 | 2018 |
Delete, retrieve, generate: a simple approach to sentiment and style transfer J Li, R Jia, H He, P Liang arXiv preprint arXiv:1804.06437, 2018 | 599 | 2018 |
Single image super-resolution using Gaussian process regression H He, WC Siu CVPR 2011, 449-456, 2011 | 383 | 2011 |
Sharp nearby, fuzzy far away: How neural language models use context U Khandelwal, H He, P Qi, D Jurafsky arXiv preprint arXiv:1805.04623, 2018 | 367 | 2018 |
FEQA: A question answering evaluation framework for faithfulness assessment in abstractive summarization E Durmus, H He, M Diab arXiv preprint arXiv:2005.03754, 2020 | 361 | 2020 |
Opponent modeling in deep reinforcement learning H He, J Boyd-Graber, K Kwok, H Daumé III International conference on machine learning, 1804-1813, 2016 | 354 | 2016 |
Learning to search in branch and bound algorithms H He, H Daume III, JM Eisner Advances in neural information processing systems 27, 2014 | 286 | 2014 |
Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings H He, A Balakrishnan, M Eric, P Liang Association for Computational Linguistics (ACL), 2017 | 248 | 2017 |
Gluoncv and gluonnlp: Deep learning in computer vision and natural language processing J Guo, H He, T He, L Lausen, M Li, H Lin, X Shi, C Wang, J Xie, S Zha, ... Journal of Machine Learning Research 21 (23), 1-7, 2020 | 213 | 2020 |
Unlearn dataset bias in natural language inference by fitting the residual H He, S Zha, H Wang arXiv preprint arXiv:1908.10763, 2019 | 192 | 2019 |
An empirical study on robustness to spurious correlations using pre-trained language models L Tu, G Lalwani, S Gella, H He Transactions of the Association for Computational Linguistics 8, 621-633, 2020 | 170 | 2020 |
Decoupling strategy and generation in negotiation dialogues H He, D Chen, A Balakrishnan, P Liang arXiv preprint arXiv:1808.09637, 2018 | 151 | 2018 |
Language models are greedy reasoners: A systematic formal analysis of chain-of-thought A Saparov, H He arXiv preprint arXiv:2210.01240, 2022 | 136 | 2022 |
Imitation learning by coaching H He, J Eisner, H Daume Advances in neural information processing systems 25, 2012 | 125 | 2012 |
Don’t until the final verb wait: Reinforcement learning for simultaneous machine translation A Grissom II, H He, J Boyd-Graber, J Morgan, H Daumé III Proceedings of the 2014 Conference on empirical methods in natural language …, 2014 | 114 | 2014 |
Solving olympiad geometry without human demonstrations TH Trinh, Y Wu, QV Le, H He, T Luong Nature 625 (7995), 476-482, 2024 | 110 | 2024 |
Meta-learning via language model in-context tuning Y Chen, R Zhong, S Zha, G Karypis, H He arXiv preprint arXiv:2110.07814, 2021 | 102 | 2021 |
Types of out-of-distribution texts and how to detect them U Arora, W Huang, H He arXiv preprint arXiv:2109.06827, 2021 | 83 | 2021 |
Text generation by learning from demonstrations RY Pang, H He arXiv preprint arXiv:2009.07839, 2020 | 79 | 2020 |
Pun generation with surprise H He, N Peng, P Liang arXiv preprint arXiv:1904.06828, 2019 | 79 | 2019 |