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Reinforcement learning (RL) is frequently used to increase performance in text generation tasks, including machine translation (MT), notably through the use of Minimum Risk Training (MRT) and Generative Adversarial Networks (GAN).
Asynchronous methods for deep reinforcement learning
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Christopher JCH Watkins and Peter Dayan. 1992 · 1992
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams. 1992 · 1992
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Richard S Sutton and Andrew G Barto. 1998 · 1998
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Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
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Minimum error rate training in statistical machine translation
Franz Josef Och. 2003 · 2003
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Moses: Open source toolkit for statistical machine translation
Philipp Koehn, Hieu Hoang, Alexandra Birch, Chris Callison-Burch, Marcello Federico, Nicola Bertoldi, Brooke Cowan, Wade Shen, Christine Moran, Richard Zens, Chris Dyer, Ondrej Bojar, Alexandra Constantin, and Evan Herbst. 2007 · 2007
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Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude
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Deterministic policy gradient algorithms
David Silver, Guy Lever, Nicolas Heess, Thomas Degris, Daan Wierstra, and Martin Riedmiller. 2014 · 2014
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Findings of the 2015 workshop on statistical machine translation
Ondrej Bojar, Rajen Chatterjee, Christian Federmann, Barry Haddow, Matthias Huck, Chris Hokamp, Philipp Koehn, Varvara Logacheva, Christof Monz, Matteo Negri, Matt Post, Carolina Scarton, Lucia Specia, and Marco Turchi. 2015 · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba. 2015 · 2015
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Continuous control with deep reinforcement learning
Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra. 2015 · 2015
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Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al. 2015 · 2015
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Sequence level training with recurrent neural networks
Marc’Aurelio Ranzato, Sumit Chopra, Michael Auli, and Wojciech Zaremba. 2015 · 2015
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Neural headline generation with minimum risk training
Shiqi Shen Ayana, Zhiyuan Liu, and Maosong Sun. 2016 · 2016
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Generating visual explanations
Lisa Anne Hendricks, Zeynep Akata, Marcus Rohrbach, Jeff Donahue, Bernt Schiele, and Trevor Darrell. 2016 · 2016
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Optimization of image description metrics using policy gradient methods
Siqi Liu, Zhenhai Zhu, Ning Ye, Sergio Guadarrama, and Kevin Murphy. 2016 · 2016
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Lexicons and minimum risk training for neural machine translation: Naist-cmu at wat2016
Graham Neubig. 2016 · 2016
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Neural machine translation of rare words with subword units
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016 · 2016
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Minimum risk training for neural machine translation
Shiqi Shen, Yong Cheng, Zhongjun He, Wei He, Hua Wu, Maosong Sun, and Yang Liu. 2016 · 2016
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Generating text via adversarial training
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Adversarial neural machine translation
Lijun Wu, Yingce Xia, Li Zhao, Fei Tian, Tao Qin, Jianhuang Lai, and Tie-Yan Liu. 2017 · 2017
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Seqgan: Sequence generative adversarial nets with policy gradient
Lantao Yu, Weinan Zhang, Jun Wang, and Yong Yu. 2017 · 2017
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Thumt: An open source toolkit for neural machine translation
Jiac heng Zhang, Yanzhuo Ding, Shiqi Shen, Yong Cheng, Maosong Sun, Huanbo Luan, and Yang Liu. 2017 · 2017
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Massimo Caccia, Lucas Caccia, William Fedus, Hugo Larochelle, Joelle Pineau, and Laurent Charlin. 2018 · 2018
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Yizhe Zhang, Zhe Gan, and Lawrence Carin. 2016 · 2016
Cited alongside, same era.
Hindsight experience replay
Marcin Andrychowicz, Filip Wolski, Alex Ray, Jonas Schneider, Rachel Fong, Peter Welinder, Bob McGrew, Josh Tobin, OpenAI Pieter Abbeel, and Wojciech Zaremba. 2017 · 2017
Cited alongside, same era.
Adversarial learning for neural dialogue generation
Jiwei Li, Will Monroe, Tianlin Shi, Sébastien Jean, Alan Ritter, and Dan Jurafsky. 2017 · 2017
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Parameter space noise for exploration
Matthias Plappert, Rein Houthooft, Prafulla Dhariwal, Szymon Sidor, Richard Y Chen, Xi Chen, Tamim Asfour, Pieter Abbeel, and Marcin Andrychowicz. 2017 · 2017
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Language generation with recurrent generative adversarial networks without pre-training
O. Press, A. Bar, B. Bogin, J. Berant, and L. Wolf. 2017 · 2017
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Self-critical sequence training for image captioning
Steven J Rennie, Etienne Marcheret, Youssef Mroueh, Jerret Ross, and Vaibhava Goel. 2017 · 2017
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Grammatical error correction with neural reinforcement learning
Keisuke Sakaguchi, Matt Post, and Benjamin Van Durme. 2017 · 2017
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Classical structured prediction losses for sequence to sequence learning
Sergey Edunov, Myle Ott, Michael Auli, David Grangier, and Marc’Aurelio Ranzato. 2018 · 2018
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Dora the explorer: Directed outreaching reinforcement action-selection
Lior Fox, Leshem Choshen, and Yonatan Loewenstein. 2018 · 2018
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Divide-and-conquer reinforcement learning
Dibya Ghosh, Avi Singh, Aravind Rajeswaran, Vikash Kumar, and Sergey Levine. 2018 · 2018
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Neural transition-based string transduction for limited-resource setting in morphology
Peter Makarov and Simon Clematide. 2018 · 2018
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Xnmt: The extensible neural machine translation toolkit
Graham Neubig, Matthias Sperber, Xinyi Wang, Matthieu Felix, Austin Matthews, Sarguna Padmanabhan, Ye Qi, Devendra Singh Sachan, Philip Arthur, Pierre Godard, John Hewitt, Rachid Riad, and Liming Wang. 2018 · 2018
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A stochastic decoder for neural machine translation
Philip Schulz, Wilker Aziz, and Trevor Cohn. 2018 · 2018
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Evaluating text GANs as language models
G. Tevet, G. Habib, V. Shwartz, and J. Berant. 2018 · 2018
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A study of reinforcement learning for neural machine translation
Lijun Wu, Fei Tian, Tao Qin, Jianhuang Lai, and Tie-Yan Liu. 2018 · 2018
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Improving neural machine translation with conditional sequence generative adversarial nets
Zhen Yang, Wei Chen, Feng Wang, and Bo Xu. 2018 · 2018
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Diversity is all you need: Learning skills without a reward function
Benjamin Eysenbach, Abhishek Gupta, Julian Ibarz, and Sergey Levine. 2019 · 2019
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