Language Model Adaptation for Difficult to Translate Phrases

This paper investigates the idea of adapt- ing language models for phrases that have poor translation quality. We apply a se- lective adaptation criterion which uses a classifier to locate the most difficult phrase of each source language sentence. A spe- cial adapted language model is constructed for the highlighted phrase. Our adapta- tion heuristic uses lexical features of the phrase to locate the relevant parts of the parallel corpus for language model train- ing. As we vary the experimental setup by changing the size of the SMT training data, our adaptation method consistently shows strong improvements over the baseline sys- tems.