Fetching the paper…
Reading the bibliography…
Conventional Generative Adversarial Networks (GANs) for text generation tend to have issues of reward sparsity and mode collapse that affect the quality and diversity of generated samples.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
Policy gradient methods for reinforcement learning with function approximation
Richard S Sutton, David A McAllester, Satinder P Singh, and Yishay Mansour · 2000
Earlier work this paper cites.
Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu · 2002
Earlier work this paper cites.
The epoch-greedy algorithm for contextual multi-armed bandits
John Langford and Tong Zhang · 2007
Earlier work this paper cites.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
On distinguishability criteria for estimating generative models
Ian J Goodfellow · 2014
Earlier work this paper cites.
Convolutional neural networks for sentence classification
Yoon Kim · 2014
Earlier work this paper cites.
Scheduled sampling for sequence prediction with recurrent neural networks
Samy Bengio, Oriol Vinyals, Navdeep Jaitly, and Noam Shazeer · 2015
Earlier work this paper cites.
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
Earlier work this paper cites.
Exploring the limits of language modeling
Rafal Jozefowicz, Oriol Vinyals, Mike Schuster, Noam Shazeer, and Yonghui Wu · 2016
Cited alongside, same era.
Gans for sequences of discrete elements with the gumbel-softmax distribution
Matt J Kusner and José Miguel Hernández-Lobato · 2016
Cited alongside, same era.
Maximum-likelihood augmented discrete generative adversarial networks
Tong Che, Yanran Li, Ruixiang Zhang, R Devon Hjelm, Wenjie Li, Yangqiu Song, and Yoshua Bengio · 2017
Cited alongside, same era.
Supervised learning of universal sentence representations from natural language inference data
Alexis Conneau, Douwe Kiela, Holger Schwenk, Loic Barrault, and Antoine Bordes · 2017
Cited alongside, same era.
Seqgan: Sequence generative adversarial nets with policy gradient
Lantao Yu, Weinan Zhang, Jun Wang, and Yong Yu · 2017
Later among the works it cites.
Adversarial feature matching for text generation
Yizhe Zhang, Zhe Gan, Kai Fan, Zhi Chen, Ricardo Henao, Dinghan Shen, and Lawrence Carin · 2017
Later among the works it cites.
Adversarial text generation via feature-mover’s distance
Liqun Chen, Shuyang Dai, Chenyang Tao, Haichao Zhang, Zhe Gan, Dinghan Shen, Yizhe Zhang, Guoyin Wang, Ruiyi Zhang, and Lawrence Carin · 2018
Later among the works it cites.
Long text generation via adversarial training with leaked information
Jiaxian Guo, Sidi Lu, Han Cai, Weinan Zhang, Yong Yu, and Jun Wang · 2018
Later among the works it cites.
The relativistic discriminator: a key element missing from standard gan
Alexia Jolicoeur-Martineau · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 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
Cited alongside, same era.
Adversarial ranking for language generation
Kevin Lin, Dianqi Li, Xiaodong He, Zhengyou Zhang, and Ming-Ting Sun · 2017
Cited alongside, same era.
Self-critical sequence training for image captioning
Steven J. Rennie, Etienne Marcheret, Youssef Mroueh, Jerret Ross, and Vaibhava Goel · 2017
Cited alongside, same era.
Mastering the game of go without human knowledge
David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, et al · 2017
Cited alongside, same era.
Relgan: Relational generative adversarial networks for text generation
Weili Nie, Nina Narodytska, and Ankit Patel · 2018
Later among the works it cites.
Towards diverse text generation with inverse reinforcement learning
Zhan Shi, Xinchi Chen, Xipeng Qiu, and Xuanjing Huang · 2018
Later among the works it cites.
Texygen: A benchmarking platform for text generation models
Yaoming Zhu, Sidi Lu, Lei Zheng, Jiaxian Guo, Weinan Zhang, Jun Wang, and Yong Yu · 2018
Later among the works it cites.
Learning to compare for better training and evaluation of open domain text generation models
Wangchunshu Zhou and Ke Xu · 2020
Closest in time.