Phrase Dependency Machine Translation with Quasi-Synchronous Tree-to-Tree Features

Abstract

Recent research has shown clear improvement in translation quality by exploiting linguistic syntax for either the source or target language. However, when using syntax for both languages (“tree-to-tree” translation), there is evidence that syntactic divergence can hamper the extraction of useful rules (Ding and Palmer 2005; Ambati and Lavie 2008). Smith and Eisner (2006) introduced quasi-synchronous grammar (QG), a formalism that treats non-isomorphic structure softly using features rather than hard constraints. While a natural fit for translation modeling, its flexibility has proved challenging for building real-world systems. In this article, we present a tree-to-tree machine translation system inspired by quasi-synchronous grammar. The core of our approach is a new model that combines phrases and dependency syntax, integrating the advantages of phrase-based and syntax-based translation. We report statistically significant improvements over a phrase-based baseline on five of seven test sets across four language pairs. We also present encouraging preliminary results on the use of unsupervised dependency parsing for syntax-based translation.