2017 年 12 巻 p. 172-201
Ideally, tree-to-tree machine translation (MT) that utilizes syntactic parse trees onboth source and target sides could preserve non-local structure, and thus generatefluent and accurate translations. In practice, however, firstly, high quality parsers forboth source and target languages are difficult to obtain; secondly, even if we havehigh quality parsers on both sides, they still can be non-isomorphic because of theannotation criterion difference between the two languages. The lack of isomorphismbetween the parse trees makes it difficult to extract translation rules. This extremelylimits the performance of tree-to-tree MT. In this article, we present an approachthat projects dependency parse trees from the language side that has a high qualityparser, to the side that has a low quality parser, to improve the isomorphism of theparse trees. We first project a part of the dependencies with high confidence to makea partial parse tree, and then complement the remaining dependencies with partialparsing constrained by the already projected dependencies. Experiments conductedon the Japanese-Chinese and English-Chinese language pairs show that our proposedmethod significantly improves the performance on both the two language pairs.