Overcoming the customization bottleneck using example-based MT

Abstract

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.