Language Model Adaptation for Difficult to Translate Phrases

Presenter: Behrang Mohit
Title: Language Model Adaptation for Difficult to Translate Phrases
Date: Tuesday 12:30pm, 14 April 2009

Abstract:
We investigate the idea of adapting language models for phrases that
have poor translation quality. We apply a selective adaptation
criterion which uses a classifier to locate the most difficult phrase
of each source language sentence. A special adapted language model is
constructed for the highlighted phrase. Our adaptation heuristic uses
lexical features of the phrase to locate the relevant parts of the
parallel corpus for language model training. 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 systems.
This is a joint work with Frank Liberato and Rebecca Hwa.