Enhancing Event Causality Identification with Rationale and Structure-Aware Causal Question Answering
This work addresses document-level event causality identification, an incremental advancement for natural language processing applications.
The paper tackles the problem of identifying causal relations between events in documents by proposing a multi-task learning framework that transforms the task into multiple-choice question answering, generates rationales, and constructs an event structure graph. Experiments on two benchmark datasets show significant advantages over state-of-the-art methods, with quantitative and qualitative analyses explaining the improvements.
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.