Document-level event extraction (DEE) is a challenging task because arguments scattered across multiple sentences form variable-length event lists. Existing methods often adopt an autoregressive approach, performing event identification and argument extraction sequentially. On one hand, this limits the interaction between global event and local argument information; on the other hand, errors in event type classification can propagate to later argument extraction. To address these issues, we propose an event- and argument-aware attention mechanism to reduce error propagation, and a joint learning framework (JLF) to enhance the interaction between event and argument information. In addition, we design a complete event topology decomposition (ETD) that supports the extraction of variable-length event lists across multiple sentences. Extensive experiments show that our method achieves new state-of-the-art performance on three public datasets, with improvements of 10.6% on the ChFinAnn dataset, 5.6% on the DuEE-Fin dataset, and 14.8% on the FNDEE dataset.
Jiang et al. (Thu,) studied this question.