Paper: Alignment Model Adaptation For Domain-Specific Word Alignment

ACL ID P05-1058
Title Alignment Model Adaptation For Domain-Specific Word Alignment
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2005
Authors

This paper proposes an alignment adaptation approach to improve domain-specific (in-domain) word alignment. The basic idea of alignment adaptation is to use out-of-domain corpus to improve in-domain word alignment results. In this paper, we first train two statistical word alignment models with the large-scale out-of-domain corpus and the small-scale in-domain corpus respectively, and then interpolate these two models to improve the domain-specific word alignment. Experimental results show that our approach improves domain-specific word alignment in terms of both precision and recall, achieving a relative error rate reduction of 6.56% as compared with the state-of-the-art technologies.