Paper: Maximum Entropy Based Phrase Reordering Model For Statistical Machine Translation

ACL ID P06-1066
Title Maximum Entropy Based Phrase Reordering Model For Statistical Machine Translation
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2006
Authors

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.