Maximum Entropy Based Phrase Reordering Model for Statistical Machine Translation

We propose a novel reordering model for phrase-based statistical machine transla-tion (SMT) that uses a maximum entropy (MaxEnt) model to predicate reorderings of neighbor blocks (phrase pairs). The model provides content-dependent, hier-archical phrasal reordering with general-ization based on features automatically learned from a real-world bitext. We present an algorithm to extract all reorder-ing events of neighbor blocks from bilin-gual data. In our experiments on Chinese-to-English translation, this MaxEnt-based reordering model obtains significant im-provements in BLEU score on the NIST MT-05 and IWSLT-04 tasks.