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Using Whole Document Context in Neural Machine Translation

Christophe Servan

Abstract

In Machine Translation, considering the document as a whole can help to resolve ambiguities and inconsistencies. In this paper, we propose a simple yet promising approach to add contextual information in Neural Machine Translation. We present a method to add source context that capture the whole document with accurate boundaries, taking every word into account. We provide this additional information to a Transformer model and study the impact of our method on three language pairs. The proposed approach obtains promising results in the English-German, English-French and French-English document-level translation tasks. We observe interesting cross-sentential behaviors where the model learns to use document-level information to improve translation coherence.
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Dates and versions

hal-02316397 , version 1 (15-10-2019)

Identifiers

  • HAL Id : hal-02316397 , version 1

Cite

Valentin Macé, Christophe Servan. Using Whole Document Context in Neural Machine Translation. 16th International Workshop on Spoken Language Translation 2019, Nov 2019, Hong-Kong, China. ⟨hal-02316397⟩

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UNIV-AMU
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