Tree-to-string alignment templates for statistical machine transaltion

We present a novel translation model based on tree-to-string alignment template (TAT) which describes the alignment be-tween a source parse tree and a target string. A TAT is capable of generating both terminals and non-terminals and per-forming reordering at both low and high levels. The model is linguistically syntax-based because TATs are extracted auto-matically from word-aligned, source side parsed parallel texts. To translate a source sentence, we first employ a parser to pro-duce a source parse tree and then ap-ply TATs to transform the tree into a tar-get string. Our experiments show that the TAT-based model significantly outper-forms Pharaoh, a state-of-the-art decoder for phrase-based models