In-context Contrastive Learning for Event Causality Identification
This work addresses the challenge of identifying causal relations between events in natural language processing, offering an incremental improvement over existing prompt-based methods.
The paper tackled the problem of Event Causality Identification by proposing an In-Context Contrastive Learning model that enhances positive and negative demonstrations, achieving significant performance improvements over state-of-the-art algorithms on EventStoryLine and Causal-TimeBank corpora.
Event Causality Identification (ECI) aims at determining the existence of a causal relation between two events. Although recent prompt learning-based approaches have shown promising improvements on the ECI task, their performance are often subject to the delicate design of multiple prompts and the positive correlations between the main task and derivate tasks. The in-context learning paradigm provides explicit guidance for label prediction in the prompt learning paradigm, alleviating its reliance on complex prompts and derivative tasks. However, it does not distinguish between positive and negative demonstrations for analogy learning. Motivated from such considerations, this paper proposes an In-Context Contrastive Learning (ICCL) model that utilizes contrastive learning to enhance the effectiveness of both positive and negative demonstrations. Additionally, we apply contrastive learning to event pairs to better facilitate event causality identification. Our ICCL is evaluated on the widely used corpora, including the EventStoryLine and Causal-TimeBank, and results show significant performance improvements over the state-of-the-art algorithms.