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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 thatdoi:10.3115/1219840.1219898 dblp:conf/acl/WuWL05 fatcat:5yrwkiryc5f5ffh7h3nhlhmc44