Multi-Predicate Semantic Role Labeling

The current approaches to Semantic Role Labeling (SRL) usually perform role classification for each predicate separately and the interaction among individual predicate’s role labeling is ignored if there is more than one predicate in a sentence. In this paper, we prove that different predicates in a sentence could help each other during SRL. In multi-predicate role labeling, there are mainly two key points: argument identification and role labeling of the arguments shared by multiple predicates. To address these issues, in the stage of argument identification, we propose novel predicate-related features which help remove many argument identification errors; in the stage of argument classification, we adopt a discriminative reranking approach to perform role classification of the shared arguments, in which a large set of global features are proposed. We conducted experiments on two standard benchmarks: Chinese PropBank and English PropBank. The experimental results show that our approach can significantly improve SRL performance, especially in Chinese PropBank.