A Probabilistic Approach to Syntax-based Reordering for Statistical Machine Translation

Inspired by previous preprocessing ap- proaches 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 re- ordered inputs, which are then fed to stan- dard 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%.