Sentimental Contrastive Learning for event representation

Abstract

Event representation learning is crucial for numerous event-driven tasks, as the quality of event representations greatly influences the performance of these tasks. However, many existing event representation methods exhibit a heavy reliance on semantic features, often neglecting the wealth of information available in other dimensions of events. Consequently, these methods struggle to capture subtle distinctions between events. Incorporating sentimental information can be particularly useful when modeling event data, as leveraging such information can yield superior event representations. To effectively integrate sentimental information, we propose a novel event representation learning framework, namely Sentimental Contrastive Learning (SCL). Specifically, we firstly utilize BERT as the backbone network for pre-training and obtain the initial event representations. Subsequently, we employ instance-level and cluster-level contrastive learning to fine-tune the original event representations. We introduce two distinct contrastive losses respectively for instance-level and cluster-level contrastive learning, each aiming to incorporate sentimental information from different perspectives. To evaluate the effectiveness of our proposed model, we select the event similarity evaluation task and conduct experiments on three representative datasets. Extensive experimental results demonstrate obvious performance improvement achieved by our approach over many other models.