Paper: Coarse-to-Fine Syntactic Machine Translation using Language Projections

ACL ID D08-1012
Title Coarse-to-Fine Syntactic Machine Translation using Language Projections
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2008
Authors

The intersection of tree transducer-based translation models with n-gram language models results in huge dynamic programs for machine translation decoding. We propose a multipass, coarse-to-fine approach in which the language model complexity is incremen- tally introduced. In contrast to previous order- based bigram-to-trigram approaches, we fo- cus on encoding-based methods, which use a clustered encoding of the target language. Across various encoding schemes, and for multiplelanguagepairs,weshowspeed-upsof upto50timesoversingle-passdecodingwhile improving BLEU score. Moreover, our entire decodingcascadefortrigramlanguagemodels is faster than the corresponding bigram pass alone of a bigram-to-trigram decoder.