A Simple, Straightforward and Effective Model for Joint Bilingual Terms Detection and Word Alignment in SMT

Terms extensively exist in specific domains, and term translation plays a critical role in domain-specific statistical machine translation (SMT) tasks. However, it’s a challenging task to extract term translation knowledge from parallel sentences because of the error propagation in the SMT training pipeline. In this paper, we propose a simple, straightforward and effective model to mitigate the error propagation and improve the quality of term translation. The proposed model goes from initial weak monolingual detection of terms based on naturally annotated resources (e.g. Wikipedia) to a stronger bilingual joint detection of terms, and allows the word alignment to interact. The extensive experiments show that our method substantially boosts the performance of bilingual term detection by more than 8 points absolute F-score. And the term translation quality is substantially improved by more than 3.66% accuracy, as well as the sentence translation quality is significantly improved by 0.38 absolute BLEU points, compared with the strong baseline, i.e. the well tuned Moses.