Simultaneous word-morpheme alignment for statistical machine translation

Current word alignment models for statisti-cal machine translation do not address mor-phology beyond merely splitting words. We present a two-level alignment model that dis-tinguishes between words and morphemes, in which we embed an IBM Model 1 inside an HMM based word alignment model. The model jointly induces word and morpheme alignments using an EM algorithm. We eval-uated our model on Turkish-English parallel data. We obtained significant improvement of BLEU scores over IBM Model 4. Our results indicate that utilizing information from mor-phology improves the quality of word align-ments.