Using a Dependency Parser to Improve SMT for Subject-Object-Verb Languages

We introduce a novel precedence reordering approach based on a dependency parser to sta-tistical machine translation systems. Similar to other preprocessing reordering approaches, our method can efficiently incorporate linguis-tic knowledge into SMT systems without in-creasing the complexity of decoding. For a set of five subject-object-verb (SOV) order lan-guages, we show significant improvements in BLEU scores when translating from English, compared to other reordering approaches, in state-of-the-art phrase-based SMT systems.