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… (More)
We apply multi-rate HMMs, a tree struc-tured HMM model, to the word-alignment problem. Multi-rate HMMs allow us to model reordering at both the morpheme level and the word level in a hierarchical fashion. This approach leads to better machine translation results than a morpheme-aware model that does not explicitly model morpheme reordering.