Fetching the paper…
Reading the bibliography…
Despite low latency, non-autoregressive machine translation (NAT) suffers severe performance deterioration due to the naive independence assumption.
Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
Earlier work this paper cites.
Minimum error rate training in statistical machine translation
Franz Josef Och. 2003 · 2003
Earlier work this paper cites.
Glancing transformer for non-autoregressive neural machine translation
Lihua Qian, Hao Zhou, Yu Bao, Mingxuan Wang, Lin Qiu, Weinan Zhang, Yong Yu, and Lei Li. 2021 · 2003
Earlier work this paper cites.
Statistical significance tests for machine translation evaluation
Philipp Koehn. 2004 · 2004
Earlier work this paper cites.
ROUGE: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
Earlier work this paper cites.
A study of translation edit rate with targeted human annotation
Matthew Snover, Bonnie Dorr, Rich Schwartz, Linnea Micciulla, and John Makhoul. 2006 · 2006
Earlier work this paper cites.
The Study of Language
G. Yule. 2006 · 2006
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba. 2015 · 2015
Earlier work this paper cites.
chrF: character n-gram F-score for automatic MT evaluation
Maja Popović. 2015 · 2015
Earlier work this paper cites.
Sequence level training with recurrent neural networks
Marc’Aurelio Ranzato, Sumit Chopra, Michael Auli, and Wojciech Zaremba. 2016 · 2016
Earlier work this paper cites.
Minimum risk training for neural machine translation
Shiqi Shen, Yong Cheng, Zhongjun He, Wei He, Hua Wu, Maosong Sun, and Yang Liu. 2016 · 2016
Earlier work this paper cites.
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, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Lukasz Kaiser, Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens, George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason Smith, Jason Riesa, Alex Rudnick, Oriol Vinyals, Greg Corrado, Macduff Hughes, and Jeffrey Dean. 2016 · 2016
Earlier work this paper cites.
An actor-critic algorithm for sequence prediction
Dzmitry Bahdanau, Philemon Brakel, Kelvin Xu, Anirudh Goyal, Ryan Lowe, Joelle Pineau, Aaron C. Courville, and Yoshua Bengio. 2017 · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Earlier work this paper cites.
Understanding back-translation at scale
Sergey Edunov, Myle Ott, Michael Auli, and David Grangier. 2018a · 2018
Earlier work this paper cites.
Classical structured prediction losses for sequence to sequence learning
Sergey Edunov, Myle Ott, Michael Auli, David Grangier, and Marc’Aurelio Ranzato. 2018b · 2018
Cited alongside, same era.
Non-autoregressive neural machine translation
Jiatao Gu, James Bradbury, Caiming Xiong, Victor O. K. Li, and Richard Socher. 2018 · 2018
Cited alongside, same era.
Fast decoding in sequence models using discrete latent variables
Lukasz Kaiser, Samy Bengio, Aurko Roy, Ashish Vaswani, Niki Parmar, Jakob Uszkoreit, and Noam Shazeer. 2018 · 2018
Cited alongside, same era.
End-to-end non-autoregressive neural machine translation with connectionist temporal classification
Jindřich Libovický and Jindřich Helcl. 2018 · 2018
Cited alongside, same era.
A study of reinforcement learning for neural machine translation
Lijun Wu, Fei Tian, Tao Qin, Jianhuang Lai, and Tie-Yan Liu. 2018 · 2018
Cited alongside, same era.
Task-level curriculum learning for non-autoregressive neural machine translation
Jinglin Liu, Yi Ren, Xu Tan, Chen Zhang, Tao Qin, Zhou Zhao, and Tie-Yan Liu. 2020 · 2020
Later among the works it cites.
Non-autoregressive machine translation with latent alignments
Chitwan Saharia, William Chan, Saurabh Saxena, and Mohammad Norouzi. 2020 · 2020
Later among the works it cites.
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
Later among the works it cites.
An EM approach to non-autoregressive conditional sequence generation
Zhiqing Sun and Yiming Yang. 2020 · 2020
Later among the works it cites.
Understanding knowledge distillation in non-autoregressive machine translation
Chunting Zhou, Jiatao Gu, and Graham Neubig. 2020 · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Marjan Ghazvininejad, Omer Levy, Yinhan Liu, and Luke Zettlemoyer. 2019 · 2019
Cited alongside, same era.
Neural machine translation with adequacy-oriented learning
Xiang Kong, Zhaopeng Tu, Shuming Shi, Eduard H. Hovy, and Tong Zhang. 2019 · 2019
Cited alongside, same era.
FlowSeq: Non-autoregressive conditional sequence generation with generative flow
Xuezhe Ma, Chunting Zhou, Xian Li, Graham Neubig, and Eduard Hovy. 2019 · 2019
Cited alongside, same era.
compare-mt: A tool for holistic comparison of language generation systems
Graham Neubig, Zi-Yi Dou, Junjie Hu, Paul Michel, Danish Pruthi, and Xinyi Wang. 2019 · 2019
Cited alongside, same era.
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
Cited alongside, same era.
Retrieving sequential information for non-autoregressive neural machine translation
Chenze Shao, Yang Feng, Jinchao Zhang, Fandong Meng, Xilin Chen, and Jie Zhou. 2019 · 2019
Cited alongside, same era.
Fast structured decoding for sequence models
Zhiqing Sun, Zhuohan Li, Haoqing Wang, Di He, Zi Lin, and Zhi-Hong Deng. 2019 · 2019
Cited alongside, same era.
Yu Bao, Shujian Huang, Tong Xiao, Dongqi Wang, Xinyu Dai, and Jiajun Chen. 2021 · 2021
Later among the works it cites.
Progressive multi-granularity training for non-autoregressive translation
Liang Ding, Longyue Wang, Xuebo Liu, Derek F. Wong, Dacheng Tao, and Zhaopeng Tu. 2021a · 2021
Later among the works it cites.
Understanding and improving lexical choice in non-autoregressive translation
Liang Ding, Longyue Wang, Xuebo Liu, Derek F. Wong, Dacheng Tao, and Zhaopeng Tu. 2021c · 2021
Later among the works it cites.
Order-agnostic cross entropy for non-autoregressive machine translation
Cunxiao Du, Zhaopeng Tu, and Jing Jiang. 2021 · 2021
Later among the works it cites.
Non-autoregressive translation with layer-wise prediction and deep supervision
Chenyang Huang, Hao Zhou, Osmar R. Zaïane, Lili Mou, and Lei Li. 2021 · 2021
Later among the works it cites.
Revisiting the weaknesses of reinforcement learning for neural machine translation
Samuel Kiegeland and Julia Kreutzer. 2021 · 2021
Later among the works it cites.
AligNART: Non-autoregressive neural machine translation by jointly learning to estimate alignment and translate
Jongyoon Song, Sungwon Kim, and Sungroh Yoon. 2021 · 2021
Later among the works it cites.
GLAT: glancing at latent variables for parallel text generation
Yu Bao, Hao Zhou, Shujian Huang, Dongqi Wang, Lihua Qian, Xinyu Dai, Jiajun Chen, and Lei Li. 2022 · 2022
Closest in time.
Don’t take it literally: An edit-invariant sequence loss for text generation
Guangyi Liu, Zichao Yang, Tianhua Tao, Xiaodan Liang, Junwei Bao, Zhen Li, Xiaodong He, Shuguang Cui, and Zhiting Hu. 2022 · 2078
Closest in time.