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

Abstract

Attempts to estimate phrase translation probablities for statistical machine translation using iteratively-trained models have repeatedly failed to produce translations as good as those obtained by estimating phrase translation probablities from surface statistics of bilingual word alignments as described by Koehn, et al. (2003). We propose a new iteratively-trained phrase translation model that produces translations of quality equal to or better than those produced by Koehn, et al.'s model. Moreover, with the new model, translation quality degrades much more slowly as pruning is tightend to reduce translation time.