Better Addressing Word Deletion for Statistical Machine Translation

Word deletion (WD) problems have a critical impact on the adequacy of translation and can lead to poor comprehension of lexical meaning in the translation result. This paper studies how the word deletion problem can be handled in statistical machine translation (SMT) in detail. We classify this problem into desired and undesired word deletion based on spurious and meaningful words. Consequently, we propose four effective models to handle undesired word deletion. To evaluate word deletion problems, we develop an automatic evaluation metric that highly correlates with human judgement. Translation systems are simultaneously tuned for the proposed evaluation metric and BLEU using minimum error rate training (MERT). The experimental results demonstrate that our methods achieve significant improvements in word deletion problems on Chinese-to-English translation tasks.