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Despite its original goal to jointly learn to align and translate, prior researches suggest that Transformer captures poor word alignments through its attention mechanism.
Adding interpretable attention to neural translation models improves word alignment
Thomas Zenkel, Joern Wuebker, and John DeNero. 2019 · 1901
Earlier work this paper cites.
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
Earlier work this paper cites.
Robust bilingual word alignment for machine aided translation
Ido Dagan, Kenneth Church, and Willian Gale. 1993 · 1993
Earlier work this paper cites.
Improved statistical alignment models
Franz Josef Och and Hermann Ney. 2000 · 2000
Earlier work this paper cites.
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.
Statistical phrase-based translation
Philipp Koehn, Franz J. Och, and Daniel Marcu. 2003 · 2003
Earlier work this paper cites.
A systematic comparison of various statistical alignment models
Franz Josef Och and Hermann Ney. 2003 · 2003
Earlier work this paper cites.
Statistical significance tests for machine translation evaluation
Philipp Koehn. 2004 · 2004
Earlier work this paper cites.
Edinburgh system description for the 2005 IWSLT speech translation evaluation
Philipp Koehn, Amittai Axelrod, Alexandra Birch Mayne, Chris Callison-Burch, Miles Osborne, and David Talbot. 2005 · 2005
Earlier work this paper cites.
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Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate
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Earlier work this paper cites.
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Attention is all you need
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On the alignment problem in multi-head attention-based neural machine translation
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Eva Hasler, Adrià de Gispert, Gonzalo Iglesias, and Bill Byrne. 2018 · 2018
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Modeling coverage for neural machine translation
Zhaopeng Tu, Zhengdong Lu, Yang Liu, Xiaohua Liu, and Hang Li. 2016 · 2016
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Biasing attention-based recurrent neural networks using external alignment information
Tamer Alkhouli and Hermann Ney. 2017 · 2017
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Visualizing and understanding neural machine translation
Yanzhuo Ding, Yang Liu, Huanbo Luan, and Maosong Sun. 2017 · 2017
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What does attention in neural machine translation pay attention to?
Hamidreza Ghader and Christof Monz. 2017 · 2017
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Lexically constrained decoding for sequence generation using grid beam search
Chris Hokamp and Qun Liu. 2017 · 2017
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Saliency-driven word alignment interpretation for neural machine translation
Shuoyang Ding, Hainan Xu, and Philipp Koehn. 2019 · 2019
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Jointly learning to align and translate with transformer models
Sarthak Garg, Stephan Peitz, Udhyakumar Nallasamy, and Matthias Paulik. 2019 · 2019
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On the word alignment from neural machine translation
Xintong Li, Guanlin Li, Lemao Liu, Max Meng, and Shuming Shi. 2019 · 2019
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Lexical-constraint-aware neural machine translation via data augmentation
Guanhua Chen, Yun Chen, Yong Wang, and Victor O.K. Li. 2020 · 2020
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Alignment-enhanced transformer for constraining nmt with pre-specified translations
Kai Song, Kun Wang, Heng Yu, Yue Zhang, Zhongqiang Huang, Weihua Luo, Xiangyu Duan, and Min Zhang. 2020 · 2020
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End-to-end neural word alignment outperforms GIZA++
Thomas Zenkel, Joern Wuebker, and John DeNero. 2020 · 2020
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