用知识引导问答提升文档级事件因果关系抽取效果
Document-level Causal Relation Extraction with Knowledge-guided Binary Question Answering
- 基于事件结构构建与知识引导的二元问答框架
- 在MECI数据集上达到当前最优性能，零样本与微调均有效
- 方法具强泛化性且减少错误推理，适合复杂文本分析场景
作为信息抽取的重要任务，事件-事件因果关系抽取（ECRE）旨在识别自然语言文本中事件提及之间的因果关系。然而，现有研究存在两大挑战：缺乏文档级建模能力以及易产生因果幻觉。本文提出一种基于事件结构的知识引导二元问答方法（KnowQA），包含事件结构构建与二元问答两个阶段。我们在MECI和MAVEN-ERE数据集上，使用大语言模型在零样本与微调设置下进行了大量实验。结果表明，事件结构对文档级ECRE具有显著帮助，KnowQA方法在MECI数据集上达到当前最优表现。我们还观察到该方法不仅效果优异，且具备高泛化性与低不一致性，尤其在完成事件结构后微调模型时表现更佳。
原文摘要 · Abstract (English)
As an essential task in information extraction (IE), Event-Event Causal Relation Extraction (ECRE) aims to identify and classify the causal relationships between event mentions in natural language texts. However, existing research on ECRE has highlighted two critical challenges, including the lack of document-level modeling and causal hallucinations. In this paper, we propose a Knowledge-guided binary Question Answering (KnowQA) method with event structures for ECRE, consisting of two stages: Event Structure Construction and Binary Question Answering. We conduct extensive experiments under both zero-shot and fine-tuning settings with large language models (LLMs) on the MECI and MAVEN-ERE datasets. Experimental results demonstrate the usefulness of event structures on document-level ECRE and the effectiveness of KnowQA by achieving state-of-the-art on the MECI dataset. We observe not only the effectiveness but also the high generalizability and low inconsistency of our method, particularly when with complete event structures after fine-tuning the models.
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