Paper: Statistical Machine Translation Using Coercive Two-Level Syntactic Transduction

ACL ID W03-1002
Title Statistical Machine Translation Using Coercive Two-Level Syntactic Transduction
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2003
Authors

We define, implement and evaluate a novel model for statistical machine translation, which is based on shal- low 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 transla- tion coercion across parts of speech, as well as a transfer model based on lemma-to-lemma translation probabili- ties, which holds promise for improving machine trans- lation of low-density languages. Experiments are per- formed in both Arabic-to-English and French-to-English translation demonstrating the efficacy of the proposed techniques. Performance ...