A comparison of alignment models for statistical machine translation

In this paper, we present and compare various alignment models for statistical machine translation. We propose to measure the quality of an alignment model using the quality of the Viterbi alignment compared to a manually-produced alignment and describe a refined annotation scheme to produce suitable reference alignments. We also compare the impact of different alignment models on the translation quality of a statistical machine translation system.