Statistical machine translation using coercive two-level syntactic transduction

Abstract

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.