Incorporating target language semantic roles into a string-to-tree translation model
. School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China.. Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications, Beijing 100081, China.. Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing 100048, China
The string-to-tree model is one of the most successful syntax-based statistical machine translation (SMT) models. It models the grammaticality of the output via target-side syntax. However, it does not use any semantic information and tends to produce translations containing semantic role confusions and error chunk sequences. In this paper, we propose two methods to use semantic roles to improve the performance of the string-to-tree translation model: (1) adding role labels in the syntax tree; (2) constructing a semantic role tree, and then incorporating the syntax information into it. We then perform string-to-tree machine translation using the newly generated trees. Our methods enable the system to train and choose better translation rules using semantic information. Our experiments showed significant improvements over the state-of-the-art string-to-tree translation system on both spoken and news corpora, and the two proposed methods surpass the phrase-based system on large-scale training data.