  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.
