He He
Cited by
Cited by
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
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
Single image super-resolution using Gaussian process regression
H He, WC Siu
CVPR 2011, 449-456, 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
Opponent modeling in deep reinforcement learning
H He, J Boyd-Graber, K Kwok, H Daumé III
International conference on machine learning, 1804-1813, 2016
FEQA: A question answering evaluation framework for faithfulness assessment in abstractive summarization
E Durmus, H He, M Diab
arXiv preprint arXiv:2005.03754, 2020
Learning to search in branch and bound algorithms
H He, H Daume III, JM Eisner
Advances in neural information processing systems 27, 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
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, ...
The Journal of Machine Learning Research 21 (1), 845-851, 2020
Unlearn dataset bias in natural language inference by fitting the residual
H He, S Zha, H Wang
arXiv preprint arXiv:1908.10763, 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
Imitation learning by coaching
H He, J Eisner, H Daume
Advances in neural information processing systems 25, 2012
Decoupling strategy and generation in negotiation dialogues
H He, D Chen, A Balakrishnan, P Liang
arXiv preprint arXiv:1808.09637, 2018
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
Besting the quiz master: Crowdsourcing incremental classification games
J Boyd-Graber, B Satinoff, H He, H Daumé III
Proceedings of the 2012 joint conference on empirical methods in natural …, 2012
Pun generation with surprise
H He, N Peng, P Liang
arXiv preprint arXiv:1904.06828, 2019
Interpretese vs. translationese: The uniqueness of human strategies in simultaneous interpretation
H He, J Boyd-Graber, H Daumé III
Proceedings of the 2016 Conference of the North American Chapter of the …, 2016
Dynamic feature selection for dependency parsing
H He, H Daumé III, J Eisner
Proceedings of the 2013 conference on empirical methods in natural language …, 2013
Types of out-of-distribution texts and how to detect them
U Arora, W Huang, H He
arXiv preprint arXiv:2109.06827, 2021
Cost-sensitive dynamic feature selection
H He, H Daumé III, J Eisner
ICML Inferning Workshop, 2012
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