Tuning Syntactically Enhanced Word Alignment for Statistical Machine Translation

We introduce a syntactically enhanced word alignment model that is more flexible than state-of-the-art generative word alignment models and can be tuned according to different end tasks. First of all, this model takes the advantages of both unsupervised and supervised word alignment approaches by obtaining anchor alignments from unsupervised generative models and seeding the anchor alignments into a supervised discriminative model. Second, this model offers the flexibility of tuning the alignment according to different optimisation criteria. Our experiments show that using our word alignment in a Phrase-Based Statistical Machine Translation system yields a 5.38% relative increase on IWSLT 2007 task in terms of BLEU score.

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