A simplification-translation-restoration framework for domain adaptation in statistical machine translation: A case study in medical record translation

Integration of in-domain knowledge into an out-of-domain statistical machine translation (SMT) system poses challenges due to the lack of resources. Lack of in-domain bilingual corpora is one such issue. In this paper, we propose a simplification-translation-restoration (STR) framework for domain adaptation in SMT systems. An SMT system to translate medical records from English to Chinese is taken as a case study. We identify the critical segments in a medical sentence and simplify them to alleviate the data sparseness problem in the out-of-domain SMT system. After translating the simplified sentence, the translations of these critical segments are restored to their proper positions. Besides the simplification pre-processing step and the restoration post-processing step, we also enhance the translation and language models in the STR framework by using pseudo bilingual corpora generated by the background MT system. In the experiments, we adapt an SMT system from a government document domain to a medical record domain. The results show the effectiveness of the STR framework.