Document-level event argument extraction is a crucial yet challenging task within the field of information extraction. Current mainstream approaches primarily focus on the information interaction between event triggers and their arguments, facing two limitations: insufficient context interaction and the ignorance of event correlations. Here, we introduce a novel framework named CARLG (Contextual Aggregation of clues and Role-based Latent Guidance), comprising two innovative components: the Contextual Clues Aggregation (CCA) and the Role-based Latent Information Guidance (RLIG). The CCA module leverages the attention weights derived from a pre-trained encoder to adaptively assimilates broader contextual information, while the RLIG module aims to capture the semantic correlations among event roles. We then instantiate the CARLG framework into two variants based on two types of current mainstream EAE approaches. Notably, our CARLG framework introduces less than 1% new parameters yet significantly improving the performance. Comprehensive experiments across the RAMS, WikiEvents, and MLEE datasets confirm the superiority of CARLG, showing significant superiority in terms of both performance and inference speed compared to major benchmarks. Further analyses demonstrate the effectiveness of the proposed modules.

翻译：文档级事件论元抽取是信息抽取领域中一项关键而具有挑战性的任务。当前主流方法主要关注事件触发词与其论元之间的信息交互，面临两个局限性：上下文交互不足以及忽略事件间的关联性。本文提出一种名为CARLG（上下文线索聚合与角色隐式引导）的新框架，包含两个创新组件：上下文线索聚合模块（CCA）和基于角色的隐式信息引导模块（RLIG）。CCA模块利用预训练编码器生成的注意力权重自适应地融合更广泛的上下文信息，而RLIG模块旨在捕捉事件角色之间的语义关联。随后，我们基于当前两类主流事件论元抽取方法，将CARLG框架实例化为两种变体。值得注意的是，CARLG框架仅引入不到1%的新参数，却显著提升了性能。在RAMS、WikiEvents和MLEE数据集上的全面实验验证了CARLG的优越性，其在性能和推理速度方面均显著优于主流基准方法。进一步的分析证明了所提模块的有效性。