Type

Conference Proceedings

Authors

Qun Liu
Andy Way
Liangyou Li
Jian Zhang

Subjects

Linguistics

Topics
parameters machine translating state of the art topic english language chinese language probability machine translation

Topic-informed neural machine translation (2016)

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.
Collections Ireland -> Dublin City University -> Publication Type = Conference or Workshop Item
Ireland -> Dublin City University -> DCU Faculties and Centres = DCU Faculties and Schools: Faculty of Engineering and Computing: School of Computing
Ireland -> Dublin City University -> DCU Faculties and Centres = Research Initiatives and Centres: ADAPT
Ireland -> Dublin City University -> Status = Published
Ireland -> Dublin City University -> Subject = Computer Science: Machine translating

Full list of authors on original publication

Qun Liu, Andy Way, Liangyou Li, Jian Zhang

Experts in our system

1
Qun Liu
Dublin City University
Total Publications: 31
 
2
Andy Way
Dublin City University
Total Publications: 229
 
3
Liangyou Li
Dublin City University
Total Publications: 9
 
4
Jian Zhang
Dublin City University
Total Publications: 4