Fetching the paper…
Reading the bibliography…
Previous works have shown that contextual information can improve the performance of 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.
Statistical significance tests for machine translation evaluation
Philipp Koehn. 2004 · 2004
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.
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.
Neural Machine Translation of Rare Words with Subword Units
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016 · 2016
Earlier work this paper cites.
Graph convolutional encoders for syntax-aware neural machine translation
Joost Bastings, Ivan Titov, Wilker Aziz, Diego Marcheggiani, and Khalil Sima’an. 2017 · 2017
Earlier work this paper cites.
Encoding sentences with graph convolutional networks for semantic role labeling
Diego Marcheggiani and Ivan Titov. 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, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Earlier work this paper cites.
Exploiting Cross-Sentence Context for Neural Machine Translation
Longyue Wang, Zhaopeng Tu, Andy Way, and Qun Liu. 2017 · 2017
Earlier work this paper cites.
Evaluating Discourse Phenomena in Neural Machine Translation
Rachel Bawden, Rico Sennrich, Alexandra Birch, and Barry Haddow. 2018 · 2018
Earlier work this paper cites.
Modeling Coherence for Neural Machine Translation with Dynamic and Topic Caches
Shaohui Kuang, Deyi Xiong, Weihua Luo, and Guodong Zhou. 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.
Document Context Neural Machine Translation with Memory Networks
Sameen Maruf and Gholamreza Haffari. 2018 · 2018
Cited alongside, same era.
Document-Level Neural Machine Translation with Hierarchical Attention Networks
Lesly Miculicich, Dhananjay Ram, Nikolaos Pappas, and James Henderson. 2018 · 2018
Cited alongside, same era.
Linguistically-informed self-attention for semantic role labeling
Emma Strubell, Patrick Verga, Daniel Andor, David Weiss, and Andrew McCallum. 2018 · 2018
Cited alongside, same era.
Learning to remember translation history with a continuous cache
Zhaopeng Tu, Yang Liu, Shuming Shi, and Tong Zhang. 2018 · 2018
Cited alongside, same era.
Context-Aware Neural Machine Translation Learns Anaphora Resolution
Elena Voita, Pavel Serdyukov, Rico Sennrich, and Ivan Titov. 2018 · 2018
Cited alongside, same era.
Modeling Localness for Self-Attention Networks
Selective Attention for Context-aware Neural Machine Translation
Sameen Maruf, André FT Martins, and Gholamreza Haffari. 2019 · 2019
Later among the works it cites.
fairseq: A Fast, Extensible Toolkit for Sequence Modeling
Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, and Michael Auli. 2019 · 2019
Later among the works it cites.
Analysing Concatenation Approaches to Document-Level NMT in Two Different Domains
Yves Scherrer, Jörg Tiedemann, and Sharid Loáiciga. 2019 · 2019
Later among the works it cites.
Hierarchical Modeling of Global Context for Document-Level Neural Machine Translation
Xin Tan, Longyin Zhang, Deyi Xiong, and Guodong Zhou. 2019 · 2019
Later among the works it cites.
Leveraging local and global patterns for self-attention networks
Mingzhou Xu, Derek F Wong, Baosong Yang, Yue Zhang, and Lidia S Chao. 2019 · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Baosong Yang, Zhaopeng Tu, Derek F Wong, Fandong Meng, Lidia S Chao, and Tong Zhang. 2018 · 2018
Cited alongside, same era.
Improving the Transformer Translation Model with Document-Level Context
Jiacheng Zhang, Huanbo Luan, Maosong Sun, Feifei Zhai, Jingfang Xu, Min Zhang, and Yang Liu. 2018 · 2018
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.
Text generation from knowledge graphs with graph transformers
Rik Koncel-Kedziorski, Dhanush Bekal, Yi Luan, Mirella Lapata, and Hannaneh Hajishirzi. 2019 · 2019
Cited alongside, same era.
Deep mask memory network with semantic dependency and context moment for aspect level sentiment classification
Peiqin Lin, Meng Yang, and Jianhuang Lai. 2019 · 2019
Cited alongside, same era.
Using whole document context in neural machine translation
Valentin Macé and Christophe Servan. 2019 · 2019
Cited alongside, same era.
Zhengxin Yang, Jinchao Zhang, Fandong Meng, Shuhao Gu, Yang Feng, and Jie Zhou. 2019 · 2019
Later among the works it cites.
Improving Deep Transformer with Depth-Scaled Initialization and Merged Attention
Biao Zhang, Ivan Titov, and Rico Sennrich. 2019 · 2019
Later among the works it cites.
Knowledge graph-augmented abstractive summarization with semantic-driven cloze reward
Luyang Huang, Lingfei Wu, and Lu Wang. 2020 · 2020
Closest in time.
Dynamic Context Selection for Document-level Neural Machine Translation via Reinforcement Learning
Xiaomian Kang, Yang Zhao, Jiajun Zhang, and Chengqing Zong. 2020 · 2020
Closest in time.
Does Multi-Encoder Help? A Case Study on Context-Aware Neural Machine Translation
Bei Li, Hui Liu, Ziyang Wang, Yufan Jiang, Tong Xiao, Jingbo Zhu, Tongran Liu, and Changliang Li. 2020 · 2020
Closest in time.
A Simple and Effective Unified Encoder for Document-Level Machine Translation
Shuming Ma, Dongdong Zhang, and Ming Zhou. 2020 · 2020
Closest in time.
Towards Making the Most of Context in Neural Machine Translation
Zaixiang Zheng, Xiang Yue, Shujian Huang, Jiajun Chen, and Alexandra Birch. 2020 · 2020
Closest in time.