1 research outputs found
Domain Adaptation for Statistical Machine Translation
Statistical machine translation (SMT) systems perform poorly when it is
applied to new target domains. Our goal is to explore domain adaptation
approaches and techniques for improving the translation quality of
domain-specific SMT systems. However, translating texts from a specific domain
(e.g., medicine) is full of challenges. The first challenge is ambiguity. Words
or phrases contain different meanings in different contexts. The second one is
language style due to the fact that texts from different genres are always
presented in different syntax, length and structural organization. The third
one is the out-of-vocabulary words (OOVs) problem. In-domain training data are
often scarce with low terminology coverage. In this thesis, we explore the
state-of-the-art domain adaptation approaches and propose effective solutions
to address those problems.Comment: M.Sc Degres Thesi