Interactive-Predictive Machine Translation based on Syntactic Constraints of Prefix

Na Ye, Guiping Zhang, Dongfeng Cai


Abstract
Interactive-predictive machine translation (IPMT) is a translation mode which combines machine translation technology and human behaviours. In the IPMT system, the utilization of the prefix greatly affects the interaction efficiency. However, state-of-the-art methods filter translation hypotheses mainly according to their matching results with the prefix on character level, and the advantage of the prefix is not fully developed. Focusing on this problem, this paper mines the deep constraints of prefix on syntactic level to improve the performance of IPMT systems. Two syntactic subtree matching rules based on phrase structure grammar are proposed to filter the translation hypotheses more strictly. Experimental results on LDC Chinese-English corpora show that the proposed method outperforms state-of-the-art phrase-based IPMT system while keeping comparable decoding speed.
Anthology ID:
C16-1169
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:
1797–1806
Language:
URL:
https://aclanthology.org/C16-1169
DOI:
Bibkey:
Cite (ACL):
Na Ye, Guiping Zhang, and Dongfeng Cai. 2016. Interactive-Predictive Machine Translation based on Syntactic Constraints of Prefix. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 1797–1806, Osaka, Japan. The COLING 2016 Organizing Committee.
Cite (Informal):
Interactive-Predictive Machine Translation based on Syntactic Constraints of Prefix (Ye et al., COLING 2016)
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PDF:
https://aclanthology.org/C16-1169.pdf