A Dependency Edge-based Transfer Model for Statistical Machine Translation

Previous models in syntax-based statistical machine translation usually resort to some kinds of synchronous procedures, few of these works are based on the analysis-transfer-generation methodology. In this paper, we present a statistical implementation of the analysis-transfergeneration methodology in rule-based translation. The procedures of syntax analysis, syntax transfer and language generation are modeled independently in order to break the synchronous constraint, resorting to dependency structures with dependency edges as atomic manipulating units. Large-scale experiments on Chinese to English translation show that our model exhibits state-of-the-art performance by significantly outperforming the phrase-based model. The statistical transfer-generation method results in significantly better performance with much smaller models.