Fetching the paper…
Reading the bibliography…
Diverse machine translation aims at generating various target language translations for a given source language sentence.
Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. 2014 · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba. 2015 · 2015
Earlier work this paper cites.
A simple, fast diverse decoding algorithm for neural generation
Jiwei Li, Will Monroe, and Dan Jurafsky. 2016 · 2016
Earlier work this paper cites.
Edinburgh neural machine translation systems for WMT 16
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016 · 2016
Earlier work this paper cites.
Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, and Zbigniew Wojna. 2016 · 2016
Earlier work this paper cites.
Diverse beam search: Decoding diverse solutions from neural sequence models
Ashwin K Vijayakumar, Michael Cogswell, Ramprasath R Selvaraju, Qing Sun, Stefan Lee, David Crandall, and Dhruv Batra. 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, et al. 2016 · 2016
Earlier work this paper cites.
Convolutional sequence to sequence learning
Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, and Yann N. Dauphin. 2017 · 2017
Cited alongside, same era.
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
Cited alongside, same era.
Sequence to sequence mixture model for diverse machine translation
Xuanli He, Gholamreza Haffari, and Mohammad Norouzi. 2018 · 2018
Cited alongside, same era.
Deterministic non-autoregressive neural sequence modeling by iterative refinement
Jason Lee, Elman Mansimov, and Kyunghyun Cho. 2018 · 2018
Cited alongside, same era.
Scaling neural machine translation
Myle Ott, Sergey Edunov, David Grangier, and Michael Auli. 2018 · 2018
Cited alongside, same era.
Mixture models for diverse machine translation: Tricks of the trade
Tianxiao Shen, Myle Ott, Michael Auli, and Marc’Aurelio Ranzato. 2019 · 2019
Lost in translation: Loss and decay of linguistic richness in machine translation
Eva Vanmassenhove, Dimitar Shterionov, and Andy Way. 2019 · 2019
Later among the works it cites.
Token-level adaptive training for neural machine translation
Shuhao Gu, Jinchao Zhang, Fandong Meng, Yang Feng, Wanying Xie, Jie Zhou, and Dong Yu. 2020 · 2020
Later among the works it cites.
Sequence-level mixed sample data augmentation
Demi Guo, Yoon Kim, and Alexander Rush. 2020 · 2020
Later among the works it cites.
Generating diverse translation by manipulating multi-head attention
Zewei Sun, Shujian Huang, Hao-Ran Wei, Xin-yu Dai, and Jiajun Chen. 2020 · 2020
Later among the works it cites.
Generating diverse translation from model distribution with dropout
Xuanfu Wu, Yang Feng, and Chenze Shao. 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…
Cited alongside, same era.
Generating diverse translations with sentence codes
Raphael Shu, Hideki Nakayama, and Kyunghyun Cho. 2019 · 2019
Cited alongside, same era.
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger, and Yoav Artzi. 2020 · 2020
Later among the works it cites.