Enhancing Event Causality Identification with Rationale and
  Structure-Aware Causal Question Answering
Baiyan Zhang Qin Chen Jie Zhou Jian Jin Liang He
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.