Multi-turn and Multi-Granularity Reader for Document-level Event Extraction

Most existing event extraction (EE) works mainly focus on extracting events from one sentence. However, in real-world applications, arguments of one event may scatter across sentences and multiple events may co-occur in one document. Thus these scenarios require document-level event extraction (DEE) which aims to extract events and their arguments across sentences from a document. Previous works cast DEE as a two-step paradigm: sentence-level event argument extraction (SEE) to document-level event fusion. However, this paradigm lacks integrating document-level information for SEE and suffers from the inherent limitations of error propagation. In this paper, we propose a multi-turn and multi-granularity reader for DEE that can extract events from the document directly without the stage of preliminary SEE. Specifically, we propose a new paradigm of DEE by formulating it as a machine reading comprehension (MRC) task (i.e., the extraction of event arguments is transformed to identify the answer span from the document). Beyond the framework of MRC, we introduce a multi-turn and multi-granularity reader to capture the dependencies between arguments explicitly and model long texts effectively. The empirical results demonstrate that our method achieves superior performance on the MUC-4 and the ChFinAnn datasets.