End-to-end Semantic Role Labeling with Neural Transition-based Model
Keywords:Syntax -- Tagging, Chunking & Parsing
AbstractEnd-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.
How to Cite
Fei, H., Zhang, M., Li, B., & Ji, D. (2021). End-to-end Semantic Role Labeling with Neural Transition-based Model. Proceedings of the AAAI Conference on Artificial Intelligence, 35(14), 12803-12811. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17515
AAAI Technical Track on Speech and Natural Language Processing I