Abstract
Event argument extraction is a subtask of information extraction. Recent efforts have predominantly focused on mitigating the issue of error propagation associated with pipeline methods for extracting event arguments, such as machine reading comprehension and generative approaches. However, these aforementioned methods necessitate the careful design of various templates, and the choice of templates can significantly impact the model’s performance. Therefore, we propose a novel approach to extract event arguments using contrastive learning. Our approach aims to maximize the semantic similarity between role name semantics and actual argument semantics while minimizing the similarity between role name semantics and the semantics of other non-argument words, thereby enabling more precise extraction of argument boundaries. We investigate the impact of different templates on event argument extraction, and experimental results demonstrate that template adjustments have limited effects on our model. To attain more precise argument boundaries, we also introduce entity type boundary embeddings, which substantially enhance the effectiveness of event argument extraction.
This work was supported by the Science and Technology Program project of Shanghai Municipal Committee of Science and Technology (Grants: 22511104800 and 22DZ1204903).
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Yao, C., Guo, Y., Chen, X., Duan, Z., Fu, J. (2024). Role-Guided Contrastive Learning for Event Argument Extraction. In: Goharian, N., et al. Advances in Information Retrieval. ECIR 2024. Lecture Notes in Computer Science, vol 14608. Springer, Cham. https://doi.org/10.1007/978-3-031-56027-9_21
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