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Text generation is a crucial task in NLP.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
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Brian D Ziebart, Andrew L Maas, J Andrew Bagnell, and Anind K Dey · 2008
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Alex Graves · 2013
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Scheduled sampling for sequence prediction with recurrent neural networks
Samy Bengio, Oriol Vinyals, Navdeep Jaitly, and Noam Shazeer · 2015
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Microsoft coco captions: Data collection and evaluation server
Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollár, and C Lawrence Zitnick · 2015
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Luke Metz, Ben Poole, David Pfau, and Jascha Sohl-Dickstein · 2016
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Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
Adversarial ranking for language generation
Kevin Lin, Dianqi Li, Xiaodong He, Ming-ting Sun, and Zhengyou Zhang · 2017
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Bridging the gap between value and policy based reinforcement learning
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Text generation based on generative adversarial nets with latent variable
Heng Wang, Zengchang Qin, and Tao Wan · 2017
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SeqGAN: Sequence generative adversarial nets with policy gradient
Lantao Yu, Weinan Zhang, Jun Wang, and Yong Yu · 2017
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Adversarial feature matching for text generation
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Maximum-likelihood augmented discrete generative adversarial networks
Tong Che, Yanran Li, Ruixiang Zhang, R Devon Hjelm, Wenjie Li, Yangqiu Song, and Yoshua Bengio · 2017
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Long text generation via adversarial training with leaked information
Jiaxian Guo, Sidi Lu, Han Cai, Weinan Zhang, Yong Yu, and Jun Wang · 2017
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Yizhe Zhang, Zhe Gan, Kai Fan, Zhi Chen, Ricardo Henao, Dinghan Shen, and Lawrence Carin · 2017
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Toward learning better metrics for sequence generation training with policy gradient
Kotaro Nakayama Yutaka Matsuo Joji Toyama, Yusuke Iwasawa · 2018
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Incorporating discriminator in sentence generation: a gibbs sampling method
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