Using Semantic Structure to Improve Chinese-English Term Translation

This paper introduces a method which aims at translating Chinese terms into English. Our motivation is providing deep semantic-level information for term translation through analyzing the semantic structure of terms. Using the contextual information in the term and the first sememe of each word in HowNet as features, we trained a Support Vector Machine (SVM) model to identify the dependencies among words in a term. Then a Conditional Random Field (CRF) model is trained to mark semantic relations for term dependencies. During translation, the semantic relations within the Chinese terms are identified and three features based on semantic structure are integrated into the phrase-based statistical machine translation system. Experimental results show that the proposed method achieves 1.58 BLEU points improvement in comparison with the baseline system.