A Topic-Based Reordering Model for Statistical Machine Translation

Reordering models are one of essential components of statistical machine translation. In this paper, we propose a topic-based reordering model to predict orders for neighboring blocks by capturing topic-sensitive reordering patterns. We automatically learn reordering examples from bilingual training data, which are associated with document-level and word-level topic information induced by LDA topic model. These learned reordering examples are used as evidences to train a topic-based reordering model that is built on a maximum entropy (MaxEnt) classifier. We conduct large-scale experiments to validate the effectiveness of the proposed topic-based reordering model on the NIST Chinese-to-English translation task. Experimental results show that our topic-based reordering model achieves significant performance improvement over the conventional reordering model using only lexical information.