Simultaneous Word-Morpheme Alignment for Statistical Machine Translation

Current word alignment models for statistical machine translation do not address morphology beyond merely splitting words. We present a two-level alignment model that distinguishes 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 evaluated 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 morphology improves the quality of word alignments.