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In encoder-decoder neural models, multiple encoders are in general used to represent the contextual information in addition to the individual sentence.
Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey E. Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014 · 1958
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A maximum entropy approach to natural language processing
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Wit 3 : Web inventory of transcribed and translated talks
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Document context neural machine translation with memory networks
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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
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When and why is document-level context useful in neural machine translation?
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Selective attention for context-aware neural machine translation
Sameen Maruf, André F. T. Martins, and Gholamreza Haffari. 2019 · 2019
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Document-level neural machine translation with hierarchical attention networks
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Learning to remember translation history with a continuous cache
Zhaopeng Tu, Yang Liu, Shuming Shi, and Tong Zhang. 2018 · 2018
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Revisiting low-resource neural machine translation: A case study
Rico Sennrich and Biao Zhang. 2019 · 2019
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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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