期刊名称：Conference on European Chapter of the Association for Computational Linguistics (EACL)
出版年度：2012
卷号：2012
出版社：ACL Anthology
摘要：To adapt a translation model trained from
the data in one domain to another, previous
works paid more attention to the studies of
parallel corpus while ignoring the in-domain
monolingual corpora which can be obtained
more easily. In this paper, we propose a
novel approach for translation model adaptation
by utilizing in-domain monolingual topic
information instead of the in-domain bilingual
corpora, which incorporates the topic information
into translation probability estimation.
Our method establishes the relationship
between the out-of-domain bilingual corpus
and the in-domain monolingual corpora via
topic mapping and phrase-topic distribution
probability estimation from in-domain monolingual
corpora. Experimental result on the
NIST Chinese-English translation task shows
that our approach significantly outperforms
the baseline system.
