To understand a document with multiple events, event-event relation extraction (ERE) emerges as a crucial task, aiming to discern how natural events temporally or structurally associate with each other. To achieve this goal, our work addresses the problems of temporal event relation extraction (TRE) and subevent relation extraction (SRE). The latest methods for such problems have commonly built document-level event graphs for global reasoning across sentences. However, the edges between events are usually derived from external tools heuristically, which are not always reliable and may introduce noise. Moreover, they are not capable of preserving logical constraints among event relations, e.g., coreference constraint, symmetry constraint and conjunction constraint. These constraints guarantee coherence between different relation types,enabling the generation of a uniffed event evolution graph. In this work, we propose a novel method named LogicERE, which performs high-order event relation reasoning through modeling logic constraints. Speciffcally, different from conventional event graphs, we design a logic constraint induced graph (LCG) without any external tools. LCG involves event nodes where the interactions among them can model the coreference constraint, and event pairs nodes where the interactions among them can retain the symmetry constraint and conjunction constraint. Then we perform high-order reasoning on LCG with relational graph transformer to obtain enhanced event and event pair embeddings. Finally, we further incorporate logic constraint information via a joint logic learning module. Extensive experiments demonstrate the effectiveness of the proposed method with state-of-the-art performance on benchmark datasets.

翻译：为理解包含多个事件的文档，事件-事件关系抽取（ERE）已成为一项关键任务，旨在识别自然事件之间如何通过时间或结构相互关联。为实现这一目标，本研究针对时序事件关系抽取（TRE）与子事件关系抽取（SRE）问题展开研究。当前最先进的方法通常通过构建文档级事件图来实现跨句子的全局推理。然而，事件间的边往往基于启发式外部工具生成，其可靠性不足且可能引入噪声。此外，现有方法难以保持事件关系间的逻辑约束（例如共指约束、对称约束与合取约束），而这些约束能保障不同关系类型间的连贯性，从而生成统一的事件演化图。本文提出一种名为LogicERE的创新方法，通过建模逻辑约束实现高阶事件关系推理。具体而言，与传统事件图不同，我们设计了无需外部工具的逻辑约束引导图（LCG）。LCG包含两类节点：事件节点（其交互可建模共指约束）和事件对节点（其交互可保持对称约束与合取约束）。随后，我们通过关系图Transformer在LCG上进行高阶推理，以获取增强的事件及事件对嵌入表示。最后，通过联合逻辑学习模块进一步整合逻辑约束信息。大量实验证明，该方法在基准数据集上取得了最先进的性能表现，验证了其有效性。