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In Neural Machine Translation (and, more generally, conditional language modeling), the generation of a target token is influenced by two types of context: the source and the prefix of the target sequence.
A lightweight evaluation framework for machine translation reordering
David Talbot, Hideto Kazawa, Hiroshi Ichikawa, Jason Katz-Brown, Masakazu Seno, and Franz Och. 2011 · 2011
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On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Sebastian Bach, Alexander Binder, Grégoire Montavon, Frederick Klauschen, Klaus-Robert Müller, and Wojciech Samek. 2015 · 2015
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Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba. 2015 · 2015
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton. 2016 · 2016
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Layer-wise relevance propagation for neural networks with local renormalization layers
Alexander Binder, Grégoire Montavon, Sebastian Lapuschkin, Klaus-Robert Müller, and Wojciech Samek. 2016 · 2016
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Sequence level training with recurrent neural networks
Marc’Aurelio Ranzato, Sumit Chopra, Michael Auli, and Wojciech Zaremba. 2016 · 2016
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Neural machine translation of rare words with subword units
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016 · 2016
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Minimum risk training for neural machine translation
Shiqi Shen, Yong Cheng, Zhongjun He, Wei He, Hua Wu, Maosong Sun, and Yang Liu. 2016 · 2016
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A causal framework for explaining the predictions of black-box sequence-to-sequence models
David Alvarez-Melis and Tommi Jaakkola. 2017 · 2017
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What do neural machine translation models learn about morphology?
Yonatan Belinkov, Nadir Durrani, Fahim Dalvi, Hassan Sajjad, and James Glass. 2017 · 2017
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Evaluating the morphological competence of machine translation systems
Franck Burlot and François Yvon. 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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Curriculum learning and minibatch bucketing in neural machine translation
Tom Kocmi and Ondřej Bojar. 2017 · 2017
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Six challenges for neural machine translation
Philipp Koehn and Rebecca Knowles. 2017 · 2017
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Improving word sense disambiguation in neural machine translation with sense embeddings
Annette Rios Gonzales, Laura Mascarell, and Rico Sennrich. 2017 · 2017
Cited alongside, same era.
How grammatical is character-level neural machine translation? assessing MT quality with contrastive translation pairs
Rico Sennrich. 2017 · 2017
Cited alongside, same era.
Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan. 2017 · 2017
Cited alongside, same era.
Context gates for neural machine translation
Zhaopeng Tu, Yang Liu, Zhengdong Lu, Xiaohua Liu, and Hang Li. 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.
Using monolingual data in neural machine translation: a systematic study
Context-aware neural machine translation learns anaphora resolution
Elena Voita, Pavel Serdyukov, Rico Sennrich, and Ivan Titov. 2018 · 2018
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Neural machine translation with decoding history enhanced attention
Mingxuan Wang, Jun Xie, Zhixing Tan, Jinsong Su, Deyi Xiong, and Chao Bian. 2018 · 2018
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Identifying and controlling important neurons in neural machine translation
Anthony Bau, Yonatan Belinkov, Hassan Sajjad, Nadir Durrani, Fahim Dalvi, and James Glass. 2019 · 2019
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Naver labs Europe’s systems for the WMT19 machine translation robustness task
Alexandre Berard, Ioan Calapodescu, and Claude Roux. 2019 · 2019
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What is one grain of sand in the desert? analyzing individual neurons in deep nlp models
Fahim Dalvi, Nadir Durrani, Hassan Sajjad, Yonatan Belinkov, Anthony Bau, and James Glass. 2019 · 2019
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Franck Burlot and François Yvon. 2018 · 2018
Cited alongside, same era.
Language production dynamics with recurrent neural networks
Jesús Calvillo and Matthew Crocker. 2018 · 2018
Cited alongside, same era.
What you can cram into a single $&!#* vector: Probing sentence embeddings for linguistic properties
Alexis Conneau, German Kruszewski, Guillaume Lample, Loïc Barrault, and Marco Baroni. 2018 · 2018
Cited alongside, same era.
Classical structured prediction losses for sequence to sequence learning
Sergey Edunov, Myle Ott, Michael Auli, David Grangier, and Marc’Aurelio Ranzato. 2018 · 2018
Cited alongside, same era.
Hallucinations in neural machine translation
Katherine Lee, Orhan Firat, Ashish Agarwal, Clara Fannjiang, and David Sussillo. 2018 · 2018
Cited alongside, same era.
An analysis of source context dependency in neural machine translation
Xutai Ma, Ke Li, and Philipp Koehn. 2018 · 2018
Cited alongside, same era.
Analyzing uncertainty in neural machine translation
Myle Ott, Michael Auli, David Grangier, and Marc’Aurelio Ranzato. 2018 · 2018
Cited alongside, same era.
The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin. 2019 · 2019
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Stabilizing the lottery ticket hypothesis
Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy, and Michael Carbin. 2019 · 2019
Later among the works it cites.
Towards understanding neural machine translation with word importance
Shilin He, Zhaopeng Tu, Xing Wang, Longyue Wang, Michael Lyu, and Shuming Shi. 2019a · 2019
Later among the works it cites.
Layer-wise relevance propagation: an overview
Grégoire Montavon, Alexander Binder, Sebastian Lapuschkin, Wojciech Samek, and Klaus-Robert Müller. 2019 · 2019
Later among the works it cites.
Analyzing multi-head self-attention: Specialized heads do the heavy lifting, the rest can be pruned
Elena Voita, David Talbot, Fedor Moiseev, Rico Sennrich, and Ivan Titov. 2019 · 2019
Later among the works it cites.
The early phase of neural network training
Jonathan Frankle, David J. Schwab, and Ari S. Morcos. 2020 · 2020
Closest in time.
Regularized context gates on transformer for machine translation
Xintong Li, Lemao Liu, Rui Wang, Guoping Huang, and Max Meng. 2020 · 2020
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On exposure bias, hallucination and domain shift in neural machine translation
Chaojun Wang and Rico Sennrich. 2020 · 2020
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Understanding knowledge distillation in non-autoregressive machine translation
Chunting Zhou, Graham Neubig, and Jiatao Gu. 2020 · 2020
Closest in time.