Translation Model Based Weighting for Phrase Extraction

Domain adaptation for statistical machine translation is the task of altering general models to improve performance on the test domain. In this work, we suggest several novel weighting schemes based on trans-lation models for adapted phrase extrac-tion. To calculate the weights, we first phrase align the general bilingual training data, then, using domain specific transla-tion models, the aligned data is scored and weights are defined over these scores. Ex-periments are performed on two translation tasks, German-to-English and Arabic-to-English translation with lectures as the tar-get domain. Different weighting schemes based on translation models are compared, and significant improvements over auto-matic translation quality are reported. In addition, we compare our work to previ-ous methods for adaptation and show sig-nificant gains.