Document-level Causal Relation Extraction with Knowledge-guided Binary Question Answering (2024)

Abstract: 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.

## introduction event-event causal relation extraction (ecre) is an essential task in information extraction (ie) that aims to identify and classify the causal relationships between event mentions in natural language texts. for example, given a sentence and an event mention pair of interest (established, bearing), an ecre model should recognize the causal relationship between them, i.e., established cause ---→ bearing (figure 1 ). ecre is regarded as a precondition of various downstream tasks, such as event knowledge graph construction @xcite , future event prediction @xcite , machine reading comprehension @xcite , and natural ## methodology following the overall framework of knowqa illustrated in figure 2 , in this section, we introduce each part of the framework, i.e., the event structure construction module and the binary question answering module, in detail. ## conclusion and future work we introduced knowqa, a novel method for ecre that formulates the task as a binary qa task with the utilization of cross-task knowledge in ie, i.e., document-level event structures, consisting of two stages: event structure construction and binary question answering. experimental results on the meci and maven-ere datasets demonstrated that the effectiveness, high generalizability, and low inconsistency of knowqa, particularly with complete event structures after fine-tuning the models. in the future, we will test our method with more event relations (e.g., temporal and subevent relations) and more languages to further validate the generalizability of our proposed method.