Handling Ambiguities of Bilingual Predicate-Argument Structures for Statistical Machine Translation

Feifei Zhai, Jiajun Zhang, Yu Zhou and Chengqing Zong

The 51st Annual Meeting of the Association for Computational Linguistics (ACL 2013)
Sofia, Bulgaria, August 4-9, 2013

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Abstract

Predicate-argument structure (PAS) has been demonstrated to be very effective in improv-ing SMT performance. However, since a source-side PAS might correspond to multiple different target-side PASs, there usually exist many PAS ambiguities during translation. In this paper, we group PAS ambiguities into two types: role ambiguity and gap ambiguity. Then we propose two novel methods to handle the two PAS ambiguities for SMT accordingly: 1) inside context integration; 2) a novel maximum entropy PAS disambiguation (MEPD) model. In this way, we incorporate rich context information of PAS for disambiguation. Then we integrate the two methods into a PAS-based translation framework. Experiments show that our approach helps to achieve significant improvements on translation quality.

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