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Document-Level Event Argument Extraction by Leveraging Redundant Information and Closed Boundary Loss
2022
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
unpublished
In document-level event argument extraction, an argument is likely to appear multiple times in different expressions in the document. The redundancy of arguments underlying multiple sentences is beneficial but is often overlooked. In addition, in event argument extraction, most entities are regarded as class "others", i.e. Universum class, which is defined as a collection of samples that do not belong to any class of interest. Universum class is composed of heterogeneous entities without
doi:10.18653/v1/2022.naacl-main.222
fatcat:kn256kegpbdljnqgtwcickbifq