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Topic-informed neural machine translation

Zhang, Jian, Li, Liangyou orcid logoORCID: 0000-0002-0279-003X, Way, Andy orcid logoORCID: 0000-0001-5736-5930 and Liu, Qun orcid logoORCID: 0000-0002-7000-1792 (2016) Topic-informed neural machine translation. In: 26th International Conference on Computational Linguistics, 13-16 Dec 2016, Osaka, Japan.

Abstract
In recent years, neural machine translation (NMT) has demonstrated state-of-the-art machine translation (MT) performance. It is a new approach to MT, which tries to learn a set of parameters to maximize the conditional probability of target sentences given source sentences. In this paper, we present a novel approach to improve the translation performance in NMT by conveying topic knowledge during translation. The proposed topic-informed NMT can increase the likelihood of selecting words from the same topic and domain for translation. Experimentally, we demonstrate that topic-informed NMT can achieve a 1.15 (3.3% relative) and 1.67 (5.4% relative) absolute improvement in BLEU score on the Chinese-to-English language pair using NIST 2004 and 2005 test sets, respectively, compared to NMT without topic information.
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: Matsumoto, Yuji and Prasad, Rashmi, (eds.) Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers. . The COLING 2016 Organizing Committee.
Publisher:The COLING 2016 Organizing Committee
Official URL:https://www.aclweb.org/anthology/C16-1170
Copyright Information:© 2016 The Authors
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:ADAPT Centre for Digital Content Technology (www.adaptcentre.ie) at Dublin City University is funded under the Science Foundation Ireland Research Centres Programme (Grant 13/RC/2106) and is co-funded under the European Regional Development Fund.
ID Code:23220
Deposited On:01 May 2019 15:32 by Thomas Murtagh . Last Modified 01 May 2019 15:32
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