Incorporating Causal Connective Prediction to Improve Event Causality Identification with Generated Explanations

Event Causality Identification ECI aims to predict the causal relation for a pair of events in text. Previous work has often combined fine-tuning with specific classifiers, which contradicts the pre-trained task of the model and fails to utilize the knowledge within the PLM. Additionally, event causality identification is a complex inference task, and relying solely on sample content makes it challenging to establish an effective inference process. To tackle these two issues, we propose a new prompt-based approach for ECI, which includes a new task, causal connective prediction, and the use of explanations generated by a large-scale language model LLM to enhance event causality identification. Initially, we direct the LLM to produce natural language explanations of target event pairs to aid prompt generation. These explanations assist the model in comprehending events and their correlation. Additionally, we develop a task for predicting causal connectives to guide the reasoning process. Furthermore, we introduce a tensor matching mechanism to capture the semantic interaction of events in context, supporting our two prompt tasks. Our experimental results on two benchmark datasets demonstrate that our method outperforms state-of-the-art models in the sentence-level ECI task.