Head- and relation-driven tree-to-tree translation using phrases in a monolingual corpus

We propose an extension of context-based machine translation (CBMT) to deal with distant language pairs such as Japanese and English, incorporating a syntactic transfer approach. Our method uses a tree structure where a node is a head and an edge is a dependency with a relation between heads. We retrieve partial trees from a monolingual corpus using a bilingual dictionary to generate candidate translation phrases, and build a tree by overlapping their heads. Word orders of a verb and its elements are decided based on a structural monolingual corpus in the target language. In our experiment with Japanese to English patent translation, human evaluation results showed that our method was better than phrase-based and hierarchical phrase-based statistical machine translation methods.