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Existing work in document-level neural machine translation commonly concatenates several consecutive sentences as a pseudo-document, and then learns inter-sentential dependencies.
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Mia Xu Chen, Orhan Firat, Ankur Bapna, Melvin Johnson, Wolfgang Macherey, George Foster, Llion Jones, Mike Schuster, Noam Shazeer, Niki Parmar, Ashish Vaswani, Jakob Uszkoreit, Lukasz Kaiser, Zhifeng Chen, Yonghui Wu, and Macduff Hughes. 2018 · 2018
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Sameen Maruf and Gholamreza Haffari. 2018 · 2018
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Zaixiang Zheng, Xiang Yue, Shujian Huang, Jiajun Chen, and Alexandra Birch. 2020 · 2020
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G-transformer for document-level machine translation
Guangsheng Bao, Yue Zhang, Zhiyang Teng, Boxing Chen, and Weihua Luo. 2021 · 2021
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Measuring and increasing context usage in context-aware machine translation
Patrick Fernandes, Kayo Yin, Graham Neubig, and André F. T. Martins. 2021 · 2021
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Selective attention for context-aware neural machine translation
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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
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Experts, errors, and context: A large-scale study of human evaluation for machine translation
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Self-attention between datapoints: Going beyond individual input-output pairs in deep learning
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A survey on document-level neural machine translation: Methods and evaluation
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Multi-hop transformer for document-level machine translation
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Batchformer: Learning to explore sample relationships for robust representation learning
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BlonDe: An automatic evaluation metric for document-level machine translation
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CoDoNMT: Modeling cohesion devices for document-level neural machine translation
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Rethinking document-level neural machine translation
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