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The state of the art in machine translation (MT) is governed by neural approaches, which typically provide superior translation accuracy over statistical approaches.
The mathematics of statistical machine translation: Parameter estimation
Peter F. Brown, Vincent J. Della Pietra, Stephen A. Della Pietra, and Robert L. Mercer. 1993 · 1993
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HMM-based word alignment in statistical translation
Stephan Vogel, Hermann Ney, and Christoph Tillmann. 1996 · 1996
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Improved statistical alignment models
Franz Josef Och and Hermann Ney. 2000 · 2000
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BLEU: A method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
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An evaluation exercise for word alignment
Rada Mihalcea and Ted Pedersen. 2003 · 2003
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A systematic comparison of various statistical alignment models
Franz Josef Och and Hermann Ney. 2003 · 2003
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Edinburgh system description for the 2005 iwslt speech translation evaluation
Philipp Koehn, Amittai Axelrod, Ra Birch Mayne, Chris Callison-burch, Miles Osborne, and David Talbot. 2005 · 2005
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AER: Do we need to “improve” our alignments?
David Vilar, Maja Popović, and Hermann Ney. 2006 · 2006
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Moses: Open source toolkit for statistical machine translation
Philipp Koehn, Hieu Hoang, Alexandra Birch, Chris Callison-Burch, Marcello Federico, Nicola Bertoldi, Brooke Cowan, Wade Shen, Christine Moran, Richard Zens, Chris Dyer, Ondrej Bojar, Alexandra Constantin, and Evan Herbst. 2007 · 2007
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Parallel implementations of word alignment tool
Qin Gao and Stephan Vogel. 2008 · 2008
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015 · 2015
Earlier work this paper cites.
Incorporating discrete translation lexicons into neural machine translation
Philip Arthur, Graham Neubig, and Satoshi Nakamura. 2016 · 2016
Earlier work this paper cites.
Guided alignment training for topic-aware neural machine translation
Wenhu Chen, Evgeny Matusov, Shahram Khadivi, and Jan-Thorsten Peter. 2016 · 2016
Cited alongside, same era.
Neural machine translation with supervised attention
Lemao Liu, Masao Utiyama, Andrew Finch, and Eiichiro Sumita. 2016 · 2016
Cited alongside, same era.
Supervised attentions for neural machine translation
Haitao Mi, Zhiguo Wang, and Abe Ittycheriah. 2016 · 2016
Cited alongside, same era.
Neural machine translation of rare words with subword units
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016 · 2016
Cited alongside, same era.
Modeling coverage for neural machine translation
Zhaopeng Tu, Zhengdong Lu, Yang Liu, Xiaohua Liu, and Hang Li. 2016 · 2016
Cited alongside, same era.
Biasing attention-based recurrent neural networks using external alignment information
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
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On the alignment problem in multi-head attention-based neural machine translation
Tamer Alkhouli, Gabriel Bretschner, and Hermann Ney. 2018 · 2018
Later among the works it cites.
Findings of the 2018 conference on machine translation (wmt18)
Ondřej Bojar, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Philipp Koehn, and Christof Monz. 2018 · 2018
Later among the works it cites.
Understanding back-translation at scale
Sergey Edunov, Myle Ott, Michael Auli, and David Grangier. 2018 · 2018
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Target foresight based attention for neural machine translation
Xintong Li, Lemao Liu, Zhaopeng Tu, Shuming Shi, and Max Meng. 2018 · 2018
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A call for clarity in reporting bleu scores
Matt Post. 2018 · 2018
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Tamer Alkhouli and Hermann Ney. 2017 · 2017
Cited alongside, same era.
Guiding neural machine translation decoding with external knowledge
Rajen Chatterjee, Matteo Negri, Marco Turchi, Marcello Federico, Lucia Specia, and Frédéric Blain. 2017 · 2017
Cited alongside, same era.
What does attention in neural machine translation pay attention to?
Hamidreza Ghader and Christof Monz. 2017 · 2017
Cited alongside, same era.
Six challenges for neural machine translation
Philipp Koehn and Rebecca Knowles. 2017 · 2017
Cited alongside, same era.
Generating alignments using target foresight in attention-based neural machine translation
Jan-Thorsten Peter, Arne Nix Nix, and Hermann Ney. 2017 · 2017
Cited alongside, same era.
Using the output embedding to improve language models
Ofir Press and Lior Wolf. 2017 · 2017
Cited alongside, same era.
Later among the works it cites.
An analysis of encoder representations in transformer-based machine translation
Alessandro Raganato and Jörg Tiedemann. 2018 · 2018
Later among the works it cites.
Linguistically-informed self-attention for semantic role labeling
Emma Strubell, Patrick Verga, Daniel Andor, David Weiss, and Andrew McCallum. 2018 · 2018
Later among the works it cites.
An analysis of attention mechanisms: The case of word sense disambiguation in neural machine translation
Gongbo Tang, Rico Sennrich, and Joakim Nivre. 2018 · 2018
Later among the works it cites.
Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter. 2019 · 2019
Closest in time.
Adding Interpretable Attention to Neural Translation Models Improves Word Alignment
Thomas Zenkel, Joern Wuebker, and John DeNero. 2019 · 2019
Closest in time.