An iteratively-trained segmentation-free phrase translation model for statistical machine translation

Attempts to estimate phrase translation probablities for statistical machine transla-tion using iteratively-trained models have repeatedly failed to produce translations as good as those obtained by estimating phrase translation probablities from surface statis-tics of bilingual word alignments as de-scribed by Koehn, et al. (2003). We pro-pose a new iteratively-trained phrase trans-lation model that produces translations of quality equal to or better than those pro-duced by Koehn, et al.’s model. Moreover, with the new model, translation quality de-grades much more slowly as pruning is tigh-tend to reduce translation time.