Tuning syntactically enhanced word alignment for Statistical Machine Translation

We introduce a syntactically enhanced word alignment model that is more flex-ible than state-of-the-art generative word alignment models and can be tuned ac-cording 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 differ-ent optimisation criteria. Our experiments show that using our word alignment in a Phrase-Based Statistical Machine Trans-lation system yields a 5.38 % relative in-crease on IWSLT 2007 task in terms of BLEU score.