Topic-Informed Neural Machine Translation

Jian Zhang, Liangyou Li, Andy Way, Qun Liu


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.
Anthology ID:
C16-1170
Volume:
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
Month:
December
Year:
2016
Address:
Osaka, Japan
Venue:
COLING
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
1807–1817
Language:
URL:
https://aclanthology.org/C16-1170
DOI:
Bibkey:
Cite (ACL):
Jian Zhang, Liangyou Li, Andy Way, and Qun Liu. 2016. Topic-Informed Neural Machine Translation. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 1807–1817, Osaka, Japan. The COLING 2016 Organizing Committee.
Cite (Informal):
Topic-Informed Neural Machine Translation (Zhang et al., COLING 2016)
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PDF:
https://preview.aclanthology.org/update-css-js/C16-1170.pdf