EBMT with external word alignment and chunk alignment

Title: EBMT with external word alignment and chunk alignment.

Who: Jae Dong Kim
When: Tuesday May 12, 12:00pm
Where: NSH 3305

Abstract: Since both EBMT and SMT are data driven methods, more accurate word alignment improves system performance in EBMT as in SMT. However, EBMT has focused on finding analogous examples while SMT has achieved plausibly accurate word alignment. For this reason, it is natural that one thinks that EBMT can benefit from using SMT word alignment. In this talk, I am going to talk about our approach to make use of more accurate external word alignment from SMT in our EBMT system. I am also going to talk about my preliminary results with chunk alignment for translation in EBMT.

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.