Abstract
Event argument extraction (EAE) aims to recognize arguments that are entities involved in events and their roles. Previous prompt-based methods focus on designing appropriate prompt template for events, while neglecting to explicitly model entities in the input, resulting in models that are not well aware of arguments. In this paper, we propose a novel Dual-Prompting Interaction approach with Entity Representation Enhancement (DPIERE) to explicitly model entities for both sentence-level and document-level EAE. 1) We design appropriate event and input prompt templates to model argument roles and entities in the input respectively so that pre-trained language model (PLM) can better capture the interaction between them. 2) We introduce a simple but effective entity representation enhancement method with adaptive selection to infuse entity knowledge into PLM. Finally, Argument span prediction is employed to predict the start and end word among the input for each role. In addition, DPIERE devises position marks in event prompt template to distinguish multiple occurrences of the same argument role. Comprehensive experimental results on three benchmarks show the effectiveness of our proposed approach.
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Our work is supported by the National Natural Science Foundation of China (61976154).
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He, R. et al. (2023). Dual-Prompting Interaction with Entity Representation Enhancement for Event Argument Extraction. In: Liu, F., Duan, N., Xu, Q., Hong, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2023. Lecture Notes in Computer Science(), vol 14303. Springer, Cham. https://doi.org/10.1007/978-3-031-44696-2_13
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DOI: https://doi.org/10.1007/978-3-031-44696-2_13
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