Machine Translation by Modeling PredicateArgument Structure Transformation
Feifei Zhai, Jiajun Zhang, Yu Zhou, Chengqing Zong · 2013
Machine translation aims to generate a target sentence that is semantically equivalent to the source sentence. However, most of current statistical machine translation models do not model the semantics of sentences. In this paper, we propose a novel translation framework based on predicate-argument structure (PAS) for its capacity on grasping the semantics and skeleton structure of sentences. By using PAS, the framework effectively models both semantics of languages and global reordering for translation. In the framework, we divide the translation process into 3 steps: (1) PAS acquisition: perform semantic role labeling (SRL) on the input sentences to acquire source-side PASs; (2) Transformation: convert source-side PASs to their target counterparts by predicate-aware PAS transformation rules; (3) Translation: first translate the predicate and arguments of PAS and then adopt a CKY-style decoding algorithm to translate the entire PAS. Experimental results show that our PAS-based translation framework significantly improves the translation performance.