Document-level event argument extraction (EAE) is a crucial but challenging subtask in information extraction. Most existing approaches focus on the interaction between arguments and event triggers, ignoring two critical points: the information of contextual clues and the semantic correlations among argument roles. In this paper, we propose the CARLG model, which consists of two modules: Contextual Clues Aggregation (CCA) and Role-based Latent Information Guidance (RLIG), effectively leveraging contextual clues and role correlations for improving document-level EAE. The CCA module adaptively captures and integrates contextual clues by utilizing context attention weights from a pre-trained encoder. The RLIG module captures semantic correlations through role-interactive encoding and provides valuable information guidance with latent role representation. Notably, our CCA and RLIG modules are compact, transplantable and efficient, which introduce no more than 1% new parameters and can be easily equipped on other span-base methods with significant performance boost. Extensive experiments on the RAMS, WikiEvents, and MLEE datasets demonstrate the superiority of the proposed CARLG model. It outperforms previous state-of-the-art approaches by 1.26 F1, 1.22 F1, and 1.98 F1, respectively, while reducing the inference time by 31%. Furthermore, we provide detailed experimental analyses based on the performance gains and illustrate the interpretability of our model.

翻译：文档级事件论元抽取（EAE）是信息抽取中一项关键但具有挑战性的子任务。现有方法大多聚焦于论元与事件触发词之间的交互，忽视了两个关键点：上下文线索信息以及论元角色间的语义关联。本文提出了CARLG模型，该模型包含两个模块：上下文线索聚合（CCA）与基于角色的潜在信息引导（RLIG），有效利用上下文线索与角色关联以改进文档级EAE。CCA模块通过利用预训练编码器的上下文注意力权重，自适应捕捉并整合上下文线索。RLIG模块通过角色交互式编码捕捉语义关联，并借助潜在角色表示提供有价值的信息引导。值得注意的是，我们的CCA和RLIG模块紧凑、可移植且高效，仅引入不超过1%的新参数，可轻松集成至其他基于跨度的方法中，显著提升性能。在RAMS、WikiEvents和MLEE数据集上的大量实验表明，所提出的CARLG模型具有优越性。相较于先前最先进方法，其在三个数据集上分别提升了1.26 F1、1.22 F1和1.98 F1，同时将推理时间降低了31%。此外，我们基于性能增益提供了详细的实验分析，并阐释了模型的可解释性。