Document-level Event Causality Identification (DECI) aims to identify causal relations between two events in documents. Recent research tends to use pre-trained language models to generate the event causal relations. Whereas, these methods are prone to the errors of sequential generation due to multiple events in a document. Moreover, the potential structures such as event coreference and related causal chain are neglected. In this paper, we propose a multi-task learning framework to enhance event causality identification with rationale and structure-aware causal question answering. Specifically, the DECI task is transformed into multiple-choice question answering, and the causes and effects of the questioned event are generated with large language models. In addition, we generate the rationales to explain why these events have causal relations. Moreover, we construct an event structure graph, which models the multi-hop potential relations for causal reasoning of the current event. Experiments on two benchmark datasets show the great advantages of our proposed approach compared to the state-of-the-art methods. Moreover, we conduct both quantitative and qualitative analyses, which shed light on why each component of our approach can lead to great improvements.

翻译：文档级事件因果关系识别旨在识别文档中两个事件之间的因果关系。近期研究倾向于使用预训练语言模型生成事件因果关系，然而这类方法因文档中存在多个事件而容易产生序列生成错误。此外，事件共指关系及因果链等潜在结构常被忽略。本文提出一种多任务学习框架，通过结合解释与结构感知的因果问答来增强事件因果关系识别。具体而言，我们将DECI任务转化为多项选择问答问题，利用大语言模型生成被问事件的原因与结果。同时，我们生成解释性文本以阐明事件间存在因果关系的依据。此外，我们构建事件结构图，对当前事件的因果推理进行多跳潜在关系建模。在两个基准数据集上的实验表明，相较于当前最优方法，我们提出的方法具有显著优势。进一步通过定量与定性分析，揭示了各组件对性能提升的贡献机理。