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
- Editors:
- Yuji Matsumoto, Rashmi Prasad
- Venue:
- COLING
- SIG:
- Publisher:
- The COLING 2016 Organizing Committee
- Note:
- Pages:
- 1807–1817
- Language:
- URL:
- https://aclanthology.org/C16-1170
- DOI:
- 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)
- PDF:
- https://preview.aclanthology.org/bionlp-24-ingestion/C16-1170.pdf