Modeling Term Translation for Document-informed Machine Translation

Fandong Meng, Deyi Xiong, Wenbin Jiang, Qun Liu

Abstract: Term translation is of great importance for statistical machine translation (SMT), especially document-informed SMT. In this paper, we investigate three issues of term translation in the context of documentinformed SMT and propose three corresponding models: (a) a term translation disambiguation model which selects desirable translations for terms in the source language with domain information, (b) a term translation consistency model that encourages consistent translations for terms with a high strength of translation consistency throughout a document, and (c) a term bracketing model that rewards translation hypotheses where bracketable source terms are translated as a whole unit. We integrate the three models into hierarchical phrase-based SMT and evaluate their effectiveness on NIST ChineseEnglish translation tasks with large-scale training data. Experiment results show that all three models can achieve significant improvements over the baseline. Additionally, we can obtain a further improvement when combining the three models.