Domain Adaptation for Statistical Machine Translation
This work addresses domain adaptation for SMT, which is important for improving translation accuracy in specialized fields like medicine, but it appears incremental as it builds on existing approaches.
The paper tackles the problem of statistical machine translation (SMT) systems performing poorly in new target domains, exploring domain adaptation approaches to improve translation quality by addressing challenges like ambiguity, language style, and out-of-vocabulary words.
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.