Hierarchical Chunk-to-String Translation

Yang Feng, Dongdong Zhang, Mu Li, Qun Liu

Abstract: We present a hierarchical chunk-to-string translation model, which can be seen as a compromise between the hierarchical phrase-based model and the tree-to-string model, to combine the merits of the two models. With the help of shallow parsing, our model learns rules consisting of words and chunks and meanwhile introduce syntax cohesion. Under the weighed synchronous context-free grammar defined by these rules, our model searches for the best translation derivation and yields target translation simultaneously. Our experiments show that our model significantly outperforms the hierarchical phrase-based model and the tree-to-string model on English-Chinese Translation tasks.