A reordering model for Vietnamese-English statistical machine translation using dependency information

Reordering is a major challenge in machine translation (MT) between two languages with significant differences in word order. In this paper, we present an approach to learn reordering rules as pre-processing step based on a dependency parser in phrase-based statistical machine translation (SMT) from Vietnamese to English. Dependency parser and transformation rules are used to reorder the source sentence and applied for systems translating Vietnamese to English. We evaluated our approach on Vietnamese-English machine translation tasks, and showed that it outperform the baseline phrase-based SMT system.