Using Whole Document Context in Neural Machine Translation

Valentin Macé, 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.
Anthology ID:
2019.iwslt-1.21
Volume:
Proceedings of the 16th International Conference on Spoken Language Translation
Month:
November 2-3
Year:
2019
Address:
Hong Kong
Editors:
Jan Niehues, Rolando Cattoni, Sebastian Stüker, Matteo Negri, Marco Turchi, Thanh-Le Ha, Elizabeth Salesky, Ramon Sanabria, Loic Barrault, Lucia Specia, Marcello Federico
Venue:
IWSLT
SIG:
SIGSLT
Publisher:
Association for Computational Linguistics
Note:
Pages:
Language:
URL:
https://aclanthology.org/2019.iwslt-1.21
DOI:
Bibkey:
Cite (ACL):
Valentin Macé and Christophe Servan. 2019. Using Whole Document Context in Neural Machine Translation. In Proceedings of the 16th International Conference on Spoken Language Translation, Hong Kong. Association for Computational Linguistics.
Cite (Informal):
Using Whole Document Context in Neural Machine Translation (Macé & Servan, IWSLT 2019)
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PDF:
https://aclanthology.org/2019.iwslt-1.21.pdf