Research on automatic acquisition of translation template based on error-driven learning method

Automatic acquisition of translation templates is important for MT system to improve its translation quality and its ability of adaptation to new domain. In this paper, translation equivalences are obtained from translation corresponding trees of bilingual sentence pairs. Error-driven learning method is employed to acquire templates from extracted equivalences. At the same time, optimization method based on automatic translation evaluation is used to clean these templates. Then they are applied to a transfer-based MT system, and "863" dialog corpus in 2003 is used for open test. Experimental results show that the performance of new acquired templates exceeds that of original ones. Combination of new acquired templates and original ones makes 5-gram Nist assessment score of open test corpus improve by 8.11%.