Fetching the paper…
Reading the bibliography…
Training efficiency is one of the main problems for Neural Machine Translation (NMT).
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.
Instance weighting for domain adaptation in nlp
Jing Jiang and ChengXiang Zhai. 2007 · 2007
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014 · 2014
Earlier work this paper cites.
On using very large target vocabulary for neural machine translation
Sébastien Jean Kyunghyun Cho, Roland Memisevic, and Yoshua Bengio. 2015 · 2015
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy. 2015 · 2015
Earlier work this paper cites.
Neural machine translation of rare words with subword units
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2015 · 2015
Earlier work this paper cites.
Systran’s pure neural machine translation systems
Josep Maria Crego, Jungi Kim, Guillaume Klein, Anabel Rebollo, Kathy Yang, Jean Senellart, Egor Akhanov, Patrice Brunelle, Aurelien Coquard, Yongchao Deng, Satoshi Enoue, Chiyo Geiss, Joshua Johanson, Ardas Khalsa, Raoum Khiari, Byeongil Ko, Catherine Kobus, Jean Lorieux, Leidiana Martins, Dang-Chuan Nguyen, Alexandra Priori, Thomas Riccardi, Natalia Segal, Christophe Servan, Cyril Tiquet, Bo Wang, Jin Yang, Dakun Zhang, Jing Zhou, and Peter Zoldan. 2016 · 2016
Cited alongside, same era.
Quantized neural networks: Training neural networks with low precision weights and activations
Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio. 2016 · 2016
Cited alongside, same era.
Neural machine translation in linear time
Nal Kalchbrenner, Lasse Espeholt, Karen Simonyan, Aaron van den Oord, Alex Graves, and Koray Kavukcuoglu. 2016 · 2016
Cited alongside, same era.
Sequence-level knowledge distillation
Yoon Kim and Alexander M Rush. 2016 · 2016
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
Closest in time.
Deep recurrent models with fast-forward connections for neural machine translatin
Jie Zhou, Ying Cao, Xuguang Wang, Peng Li, and Wei Xu. 2016 · 2016
Closest in time.
Convolutional sequence to sequence learning
Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, and Yann N Dauphin. 2017 · 2017
Closest in time.
Six challenges for neural machine translation
Philipp Koehn and Rebecca Knowles. 2017 · 2017
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Compression of neural machine translation models via pruning
Abigail See, Minh-Thang Luong, and Christopher D Manning. 2016 · 2016
Cited alongside, same era.
Minimum risk training for neural machine translation
Shiqi Shen, Yong Cheng, Zhongjun He, Wei He, Hua Wu, Maosong Sun, and Yang Liu. 2016 · 2016
Cited alongside, same era.
Guided alignment training for topic-aware neural machine translation
Wenhu Chen, Evgeny Matusov, Shahram Khadivi, and Jan-Thorsten Peter. 2016a
Cited in the paper.
Efficient training and evaluation of recurrent neural network language models for automatic speech recognition
Xie Chen, Xunying Liu, Yongqiang Wang, Mark JF Gales, and Philip C Woodland. 2016b
Cited in the paper.
Oleksii Kuchaiev and Boris Ginsburg. 2017 · 2017
Closest in time.
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
Closest in time.