Document-level event argument extraction (EAE) is a vital 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）是信息抽取中至关重要但颇具挑战性的子任务。现有方法大多聚焦于论元与事件触发词之间的交互，却忽视了上下文线索信息及论元角色间的语义关联这两个关键点。本文提出CARLG模型，该模型包含两大模块：上下文线索聚合模块（CCA）与基于角色的潜在信息引导模块（RLIG），通过有效利用上下文线索与角色关联来提升文档级EAE性能。CCA模块借助预训练编码器中的上下文注意力权重，自适应地捕获并整合上下文线索；RLIG模块则通过角色交互编码捕获语义关联，并利用潜在角色表征提供有价值的信息引导。值得注意的是，CCA与RLIG模块结构紧凑、可迁移性强且计算高效，仅引入不超过1%的新参数，即可轻松适配其他基于跨度的方法并显著提升性能。在RAMS、WikiEvents及MLEE数据集上的大量实验表明，所提出的CARLG模型具有优越性：相较于现有最先进方法，其F1值分别提升1.26、1.22和1.98，同时推理时间减少31%。此外，我们基于性能增益提供了详细的实验分析，并展示了模型的可解释性。