@inproceedings{zhang-etal-2016-topic,
title = "Topic-Informed Neural Machine Translation",
author = "Zhang, Jian and
Li, Liangyou and
Way, Andy and
Liu, Qun",
editor = "Matsumoto, Yuji and
Prasad, Rashmi",
booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
month = dec,
year = "2016",
address = "Osaka, Japan",
publisher = "The COLING 2016 Organizing Committee",
url = "https://aclanthology.org/C16-1170",
pages = "1807--1817",
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.",
}
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%0 Conference Proceedings
%T Topic-Informed Neural Machine Translation
%A Zhang, Jian
%A Li, Liangyou
%A Way, Andy
%A Liu, Qun
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F zhang-etal-2016-topic
%X 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.
%U https://aclanthology.org/C16-1170
%P 1807-1817
Markdown (Informal)
[Topic-Informed Neural Machine Translation](https://aclanthology.org/C16-1170) (Zhang et al., COLING 2016)
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.