Overcoming The Customization Bottleneck Using Example-Based MT

  • Stephen D. Richardson ,
  • ,
  • Arul Menezes ,
  • Monica Corston-Oliver

Published by Association for Computational Linguistics

Publication

We describe MSR-MT, a large-scale hybrid machine translation system under development for several language pairs. This system’s ability to acquire its primary translation knowledge automatically by parsing a bilingual corpus of hundreds of thousands of sentence pairs and aligning resulting logical forms demonstrates true promise for overcoming the so-called MT customization bottleneck. Trained on English and Spanish technical prose, a blind evaluation shows that MSR-MT’s integration of rule-based parsers, example based processing, and statistical techniques produces translations whose quality exceeds that of uncustomized commercial MT systems in this domain.