Friday, February 22, 2008
12:30 PM
CSB 703
Hao Zhang
University of Rochester
Efficient Multi-pass Decoding for Synchronous Context Free Grammars
We take a multi-pass approach to machine
translation decoding when using synchronous
context-free grammars as the translation
model and n-gram language models:
the first pass uses a bigram language model,
and the resulting parse forest is used in the
second pass to guide search with a trigram language
model. The trigram pass closes most
of the performance gap between a bigram decoder
and a much slower trigram decoder, but
takes time that is insignificant in comparison
to the bigram pass. An additional fast decoding
pass maximizing the expected count
of correct translation hypotheses increases the
BLEU score significantly.