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Neural text generation models are typically trained by maximizing log-likelihood with the sequence cross entropy (CE) loss, which encourages an exact token-by-token match between a target sequence with a generated sequence.
Latent space secrets of denoising text-autoencoders
Tianxiao Shen, Jonas Mueller, Regina Barzilay, and Tommi S. Jaakkola. 2019 · 1905
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Transfer reward learning for policy gradient-based text generation
James O’Neill and Danushka Bollegala. 2019 · 1909
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Deep encoder, shallow decoder: Reevaluating the speed-quality tradeoff in machine translation
Jungo Kasai, Nikolaos Pappas, Hao Peng, J. Cross, and Noah A. Smith. 2020 · 2006
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Minimum risk annealing for training log-linear models
David A. Smith and Jason Eisner. 2006 · 2006
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Recurrent neural network based language model
Tomás Mikolov, Martin Karafiát, Lukás Burget, Jan Cernocký, and Sanjeev Khudanpur. 2010 · 2010
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Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. 2014 · 2014
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015 · 2015
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Effective approaches to attention-based neural machine translation
Thang Luong, Hieu Pham, and Christopher D. Manning. 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 A. Riedmiller, Andreas Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, and Demis Hassabis. 2015 · 2015
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Multi30K: Multilingual English-German image descriptions
Desmond Elliott, Stella Frank, Khalil Sima’an, and Lucia Specia. 2016 · 2016
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Deep reinforcement learning for dialogue generation
Jiwei Li, Will Monroe, Alan Ritter, Dan Jurafsky, Michel Galley, and Jianfeng Gao. 2016 · 2016
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Abstractive text summarization using sequence-to-sequence rnns and beyond
Ramesh Nallapati, Bowen Zhou, Cícero Nogueira dos Santos, Çaglar Gülçehre, and Bing Xiang. 2016 · 2016
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Sequence level training with recurrent neural networks
Marc’Aurelio Ranzato, Sumit Chopra, Michael Auli, and Wojciech Zaremba. 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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Google’s neural machine translation system: Bridging the gap between human and machine translation
Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, et al. 2016 · 2016
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Toward controlled generation of text
Zhiting Hu, Zichao Yang, Xiaodan Liang, Ruslan Salakhutdinov, and Eric P. Xing. 2017 · 2017
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Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole. 2017 · 2017
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Improved image captioning via policy gradient optimization of spider
Siqi Liu, Zhenhai Zhu, Ning Ye, Sergio Guadarrama, and Kevin Murphy. 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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Get to the point: Summarization with pointer-generator networks
Abigail See, Peter J. Liu, and Christopher D. Manning. 2017 · 2017
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Style transfer from non-parallel text by cross-alignment
Tianxiao Shen, Tao Lei, Regina Barzilay, and Tommi S. Jaakkola. 2017 · 2017
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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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Differentiable lower bound for expected BLEU score
Vlad Zhukov and Maksim Kretov. 2017 · 2017
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A differentiable BLEU loss. analysis and first results
Noe Casas, José A. R. Fonollosa, and Marta R. Costa-jussà. 2018 · 2018
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Non-autoregressive neural machine translation
Jiatao Gu, James Bradbury, Caiming Xiong, Victor O. K. Li, and Richard Socher. 2018 · 2018
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Deterministic non-autoregressive neural sequence modeling by iterative refinement
fairseq: A fast, extensible toolkit for sequence modeling
Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, and Michael Auli. 2019 · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
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Fast structured decoding for sequence models
Zhiqing Sun, Zhuohan Li, Haoqing Wang, Di He, Zi Lin, and ZhiHong Deng. 2019 · 2019
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Symmetric cross entropy for robust learning with noisy labels
Yisen Wang, Xingjun Ma, Zaiyi Chen, Yuan Luo, Jinfeng Yi, and James Bailey. 2019b · 2019
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Beyond BLEU:training neural machine translation with semantic similarity
John Wieting, Taylor Berg-Kirkpatrick, Kevin Gimpel, and Graham Neubig. 2019 · 2019
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L_dmi: A novel information-theoretic loss function for training deep nets robust to label noise
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Jason Lee, Elman Mansimov, and Kyunghyun Cho. 2018 · 2018
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Delete, retrieve, generate: a simple approach to sentiment and style transfer
Juncen Li, Robin Jia, He He, and Percy Liang. 2018 · 2018
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Tilde’s parallel corpus filtering methods for WMT 2018
Mārcis Pinnis. 2018 · 2018
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Style transfer through back-translation
Shrimai Prabhumoye, Yulia Tsvetkov, Ruslan Salakhutdinov, and Alan W Black. 2018 · 2018
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Greedy search with probabilistic n-gram matching for neural machine translation
Chenze Shao, Xilin Chen, and Yang Feng. 2018 · 2018
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Structured content preservation for unsupervised text style transfer
Youzhi Tian, Zhiting Hu, and Zhou Yu. 2018 · 2018
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RtGender: A corpus for studying differential responses to gender
Rob Voigt, David Jurgens, Vinodkumar Prabhakaran, Dan Jurafsky, and Yulia Tsvetkov. 2018 · 2018
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Yilun Xu, Peng Cao, Yuqing Kong, and Yizhou Wang. 2019 · 2019
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Aligned cross entropy for non-autoregressive machine translation
Marjan Ghazvininejad, Vladimir Karpukhin, Luke Zettlemoyer, and Omer Levy. 2020 · 2020
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A probabilistic formulation of unsupervised text style transfer
Junxian He, Xinyi Wang, Graham Neubig, and Taylor Berg-Kirkpatrick. 2020 · 2020
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Improved natural language generation via loss truncation
Daniel Kang and Tatsunori B. Hashimoto. 2020 · 2020
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BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020 · 2020
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Data-to-text generation with style imitation
Shuai Lin, Wentao Wang, Zichao Yang, Xiaodan Liang, Frank F Xu, Eric Xing, and Zhiting Hu. 2020 · 2020
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Noise isn’t always negative: Countering exposure bias in sequence-to-sequence inflection models
Garrett Nicolai and Miikka Silfverberg. 2020 · 2020
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BLEURT: Learning robust metrics for text generation
Thibault Sellam, Dipanjan Das, and Ankur Parikh. 2020 · 2020
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Minimizing the bag-of-ngrams difference for non-autoregressive neural machine translation
Chenze Shao, Jinchao Zhang, Yang Feng, Fandong Meng, and Jie Zhou. 2020 · 2020
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Summarizing text on any aspects: A knowledge-informed weakly-supervised approach
Bowen Tan, Lianhui Qin, Eric Xing, and Zhiting Hu. 2020 · 2020
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Text generation with efficient (soft) Q-learning
Han Guo, Bowen Tan, Zhengzhong Liu, Eric P Xing, and Zhiting Hu. 2021 · 2021
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A causal lens for controllable text generation
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