Fetching the paper…
Reading the bibliography…
Context-aware translation can be achieved by processing a concatenation of consecutive sentences with the standard Transformer architecture.
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, Ondřej Bojar, Alexandra Constantin, and Evan Herbst. 2007 · 2007
Earlier work this paper cites.
WIT3: Web inventory of transcribed and translated talks
Mauro Cettolo, Christian Girardi, and Marcello Federico. 2012 · 2012
Earlier work this paper cites.
Discourse in Statistical Machine Translation. A Survey and a Case Study
Christian Hardmeier. 2012 · 2012
Earlier work this paper cites.
Neural machine translation of rare words with subword units
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016 · 2016
Earlier work this paper cites.
Regularizing Neural Networks by Penalizing Confident Output Distributions
Gabriel Pereyra, George Tucker, Jan Chorowski, Lukasz Kaiser, and Geoffrey Hinton. 2017 · 2017
Earlier work this paper cites.
Neural machine translation with extended context
Jörg Tiedemann and Yves Scherrer. 2017 · 2017
Earlier work this paper cites.
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
Earlier work this paper cites.
Contextual Handling in Neural Machine Translation: Look Behind, Ahead and on Both Sides
Ruchit Rajeshkumar Agrawal, Marco Turchi, and Matteo Negri. 2018 · 2018
Earlier work this paper cites.
Has machine translation achieved human parity? a case for document-level evaluation
Samuel Läubli, Rico Sennrich, and Martin Volk. 2018 · 2018
Earlier work this paper cites.
A large-scale test set for the evaluation of context-aware pronoun translation in neural machine translation
Mathias Müller, Annette Rios, Elena Voita, and Rico Sennrich. 2018 · 2018
Earlier work this paper cites.
Training Tips for the Transformer Model
Martin Popel and Ondřej Bojar. 2018 · 2018
Earlier work this paper cites.
Context-aware neural machine translation learns anaphora resolution
Elena Voita, Pavel Serdyukov, Rico Sennrich, and Ivan Titov. 2018 · 2018
Cited alongside, same era.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Cited alongside, same era.
Microsoft translator at WMT 2019: Towards large-scale document-level neural machine translation
Marcin Junczys-Dowmunt. 2019 · 2019
Cited alongside, same era.
When and why is document-level context useful in neural machine translation?
Yunsu Kim, Duc Thanh Tran, and Hermann Ney. 2019 · 2019
Cited alongside, same era.
Selective attention for context-aware neural machine translation
Sameen Maruf, André F. T. Martins, and Gholamreza Haffari. 2019 · 2019
Cited alongside, same era.
fairseq: A fast, extensible toolkit for sequence modeling
Learning to encode position for transformer with continuous dynamical model
Xuanqing Liu, Hsiang-Fu Yu, Inderjit S. Dhillon, and Cho-Jui Hsieh. 2020 · 2020
Later among the works it cites.
Document-level neural MT: A systematic comparison
António Lopes, M. Amin Farajian, Rachel Bawden, Michael Zhang, and André F. T. Martins. 2020 · 2020
Later among the works it cites.
Long-short term masking transformer: A simple but effective baseline for document-level neural machine translation
Pei Zhang, Boxing Chen, Niyu Ge, and Kai Fan. 2020 · 2020
Later among the works it cites.
Towards making the most of context in neural machine translation
Zaixiang Zheng, Xiang Yue, Shujian Huang, Jiajun Chen, and Alexandra Birch. 2020 · 2020
Later among the works it cites.
G-transformer for document-level machine translation
Guangsheng Bao, Yue Zhang, Zhiyang Teng, Boxing Chen, and Weihua Luo. 2021 · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, and Michael Auli. 2019 · 2019
Cited alongside, same era.
Improving anaphora resolution in neural machine translation using curriculum learning
Dario Stojanovski and Alexander Fraser. 2019 · 2019
Cited alongside, same era.
When a good translation is wrong in context: Context-aware machine translation improves on deixis, ellipsis, and lexical cohesion
Elena Voita, Rico Sennrich, and Ivan Titov. 2019 · 2019
Cited alongside, same era.
On context span needed for machine translation evaluation
Sheila Castilho, Maja Popović, and Andy Way. 2020 · 2020
Cited alongside, same era.
Diving deep into context-aware neural machine translation
Jingjing Huo, Christian Herold, Yingbo Gao, Leonard Dahlmann, Shahram Khadivi, and Hermann Ney. 2020 · 2020
Cited alongside, same era.
Dynamic context selection for document-level neural machine translation via reinforcement learning
Xiaomian Kang, Yang Zhao, Jiajun Zhang, and Chengqing Zong. 2020 · 2020
Cited alongside, same era.
Divide and rule: Effective pre-training for context-aware multi-encoder translation models
Lorenzo Lupo, Marco Dinarelli, and Laurent Besacier. 2022a
Cited in the paper.
Breaking the corpus bottleneck for context-aware neural machine translation with cross-task pre-training
Linqing Chen, Junhui Li, Zhengxian Gong, Boxing Chen, Weihua Luo, Min Zhang, and Guodong Zhou. 2021 · 2021
Later among the works it cites.
Measuring and increasing context usage in context-aware machine translation
Patrick Fernandes, Kayo Yin, Graham Neubig, and André F. T. Martins. 2021 · 2021
Later among the works it cites.
A Comparison of Approaches to Document-level Machine Translation
Zhiyi Ma, Sergey Edunov, and Michael Auli. 2021 · 2021
Later among the works it cites.
A baseline revisited: Pushing the limits of multi-segment models for context-aware translation
Suvodeep Majumder, Stanislas Lauly, Maria Nadejde, Marcello Federico, and Georgiana Dinu. 2022 · 2022
Later among the works it cites.
Rethinking document-level neural machine translation
Zewei Sun, Mingxuan Wang, Hao Zhou, Chengqi Zhao, Shujian Huang, Jiajun Chen, and Lei Li. 2022 · 2022
Later among the works it cites.
Principal component analysis: a review and recent developments
Ian T. Jolliffe and Jorge Cadima. 2016 · 2065
Closest in time.