Sentence type based reordering model for statistical machine translation

Abstract

Many reordering approaches have been proposed for the statistical machine translation (SMT) system.However, the information about the type of source sentence is ignored in the previous works.In this paper, we propose a group of novel reordering models based on the source sentence type for Chinese-to-English translation.In our approach, an SVM-based classifier is employed to classify the given Chinese sentences into three types: special interrogative sentences, other interrogative sentences, and non-question sentences.The different reordering models are developed oriented to the different sentence types.Our experiments show that the novel reordering models have obtained an improvement of more than 2.65% in BLEU for a phrase-based spoken language translation system.