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重要且具有挑战性的任务。当前主流方法主要聚焦于事件触发词与其论元之间的信息交互，存在两方面的局限性：上下文交互不足以及对事件关联性的忽视。为此，我们提出了一种名为CARLG（线索的上下文聚合与基于角色的潜在引导）的新框架，包含两个创新组件：上下文线索聚合模块（CCA）和基于角色的潜在信息引导模块（RLIG）。CCA模块利用预训练编码器提供的注意力权重自适应地整合更广泛的上下文信息，而RLIG模块则旨在捕捉事件角色之间的语义关联。随后，我们基于当前两种主流的EAE方法将CARLG框架实例化为两种变体。值得注意的是，我们的CARLG框架引入的新参数不足1%，却能显著提升性能。在RAMS、WikiEvents和MLEE数据集上的综合实验证实了CARLG的优越性，其在性能和推理速度方面均显著优于主要基准方法。进一步的分析验证了所提模块的有效性。