Event Causality Identification (ECI) focuses on extracting causal relations between events in texts. Existing methods for ECI primarily rely on causal features and external knowledge. However, these approaches fall short in two dimensions: (1) causal features between events in a text often lack explicit clues, and (2) external knowledge may introduce bias, while specific problems require tailored analyses. To address these issues, we propose SemDI - a simple and effective Semantic Dependency Inquiry Network for ECI. SemDI captures semantic dependencies within the context using a unified encoder. Then, it utilizes a Cloze Analyzer to generate a fill-in token based on comprehensive context understanding. Finally, this fill-in token is used to inquire about the causal relation between two events. Extensive experiments demonstrate the effectiveness of SemDI, surpassing state-of-the-art methods on three widely used benchmarks. Code is available at https://github.com/hrlics/SemDI.

翻译：事件因果关系识别（ECI）专注于提取文本中事件间的因果关系。现有ECI方法主要依赖因果特征与外部知识。然而，这些方法在两方面存在不足：（1）文本中事件间的因果特征常缺乏显式线索；（2）外部知识可能引入偏差，而特定问题需要针对性分析。为解决这些问题，我们提出SemDI——一种简单高效的语义依赖查询网络。SemDI通过统一编码器捕捉上下文内的语义依赖，随后利用完形分析器基于对上下文的综合理解生成填充词符，最终使用该填充词符查询两个事件间的因果关系。大量实验证明了SemDI的有效性，其在三个广泛使用的基准数据集上均超越了现有最优方法。代码发布于 https://github.com/hrlics/SemDI。