期刊名称：Conference on European Chapter of the Association for Computational Linguistics (EACL)
出版年度：2011
卷号：2011
出版社：ACL Anthology
摘要：We present a discriminative learning method
to improve the consistency of translations in
phrase-based Statistical Machine Translation
(SMT) systems. Our method is inspired by
Translation Memory (TM) systems which are
widely used by human translators in industrial
settings. We constrain the translation of an input
sentence using the most similar ¡®translation
example¡¯ retrieved from the TM. Differently
from previous research which used simple
fuzzy match thresholds, these constraints
are imposed using discriminative learning to
optimise the translation performance. We observe
that using this method can benefit the
SMT system by not only producing consistent
translations, but also improved translation
outputs. We report a 0.9 point improvement
in terms of BLEU score on English¨CChinese
technical documents.
