Sorting, Reasoning, and Extraction: An Easy-to-Hard Reasoning Framework for Document-Level Event Argument Extraction

Abstract

Document-level event argument extraction is a crucial task to help understand event information. Existing methods mostly ignore the different extraction difficulties of arguments, and the lack of task planning significantly affects the extraction and reasoning abilities of the model. In this paper, we innovatively analyze the difficulty of arguments and propose a novel framework for reasoning from easy to hard, aiming to use the information of simple arguments to help the extraction of difficult arguments in a human-like way. Specifically, our framework consists of three core modules: sorting, reasoning, and extraction. The sorting module first sorts the argument roles according to the current context and plans the reasoning path from easy to hard. Then, the reasoning module performs information reasoning based on the reasoning path to help capture the information of difficult arguments. Finally, the extraction module utilizes the reasoning information to complete argument extraction. Experimental results on the RAMS and WikiEvents datasets show the great advantages of our proposed approach. In particular, we obtain new state-of-the-art (SOTA) performance in multiple scenarios.

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