Titile: A Probabilistic Approach to Syntax-based Reordering for Statistical Machine

Translation (ACL2007)

Abstract: Inspired by previous preprocessing approaches to SMT, this paper proposes a novel, probabilistic approach to reordering which combines the merits of syntax and phrase-based SMT. Given a source sentence and its parse tree, our method generates, by tree operations, an n-best list of reordered inputs, which are then fed to standard phrase-based decoder to produce the optimal translation. Experiments show that, for the NIST MT-05 task of Chinese-to-English translation, the proposal leads to BLEU improvement of 1.56%.