End-to-end Semantic Role Labeling with Neural Transition-based Model

• URL: http://arxiv.org/abs/2101.00394v1
• Date: Sat, 2 Jan 2021 07:35:54 GMT
• Title: End-to-end Semantic Role Labeling with Neural Transition-based Model
• Authors: Hao Fei, Meishan Zhang, Bobo Li, Donghong Ji
• Abstract summary: End-to-end semantic role labeling (SRL) has been received increasing interest. Recent work is mostly focused on graph-based neural models. We present the first work of transition-based neural models for end-to-end SRL.
• Score: 25.921541005563856
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: End-to-end semantic role labeling (SRL) has been received increasing interest. It performs the two subtasks of SRL: predicate identification and argument role labeling, jointly. Recent work is mostly focused on graph-based neural models, while the transition-based framework with neural networks which has been widely used in a number of closely-related tasks, has not been studied for the joint task yet. In this paper, we present the first work of transition-based neural models for end-to-end SRL. Our transition model incrementally discovers all sentential predicates as well as their arguments by a set of transition actions. The actions of the two subtasks are executed mutually for full interactions. Besides, we suggest high-order compositions to extract non-local features, which can enhance the proposed transition model further. Experimental results on CoNLL09 and Universal Proposition Bank show that our final model can produce state-of-the-art performance, and meanwhile keeps highly efficient in decoding. We also conduct detailed experimental analysis for a deep understanding of our proposed model.

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