We present a derivation of the alignment template model for statistical machine translation and an implementation of the model using weighted finite state transducers. The approach we describe allows us to implement each constituent distribution of the model as a weighted finite state transducer or acceptor. We show that bitext word alignment and translation under the model can be performed with standard FSM operations involving these transducers.

We define, implement and evaluate a novel model for statistical machine translation, which is based on shallow syntactic analysis (part-of-speech tagging and phrase chunking) in both the source and target languages. It is able to model long-distance constituent motion and other syntactic phenomena without requiring a full parse in either language. We also examine aspects of lexical transfer, suggesting and exploring a concept of translation coercion across parts of speech, as well as a transfer model based on lemma-to-lemma translation probabilities, which holds promise for improving machine translation of low-density languages. Experiments are performed in both Arabic-to-English and French-to-English translation demonstrating the efficacy of the proposed techniques. Performance is automatically evaluated via the Bleu score metric.