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Exploiting cross-sentence context for neural machine translation

Wang, Longyue orcid logoORCID: 0000-0002-9062-6183, Tu, Zhaopeng, Way, Andy orcid logoORCID: 0000-0001-5736-5930 and Liu, Qun orcid logoORCID: 0000-0002-7000-1792 (2017) Exploiting cross-sentence context for neural machine translation. In: 2017 Conference on Empirical Methods in Natural Language Processing, 7-8 Sept 2017, Copenhagen, Denmark.

Abstract
In translation, considering the document as a whole can help to resolve ambiguities and inconsistencies. In this paper, we propose a cross-sentence context-aware approach and investigate the influence of historical contextual information on the performance of neural machine translation (NMT). First, this history is summarized in a hierarchical way. We then integrate the historical representation into NMT in two strategies: 1) a warm-start of encoder and decoder states, and 2) an auxiliary context source for updating decoder states. Experimental results on a large Chinese-English translation task show that our approach significantly improves upon a strong attention-based NMT system by up to +2.1 BLEU points.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Subjects:Computer Science > Machine translating
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > ADAPT
Published in: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. . Association for Computational Linguistics.
Publisher:Association for Computational Linguistics
Official URL:http://dx.doi.org/10.18653/v1/D17-1301
Copyright Information:© 2017 Association for Computational Linguistics
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Science Foundation of Ireland (SFI) ADAPT project (Grant No.:13/RC/2106).
ID Code:23337
Deposited On:21 May 2019 15:45 by Thomas Murtagh . Last Modified 21 May 2019 15:45
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