Event Representation Learning with Multi-Grained Contrastive Learning and Triple-Mixture of Experts

Abstract

Event representation learning plays a crucial role in numerous natural language processing (NLP) tasks, as it facilitates the extraction of semantic features associated with events. Current methods of learning event representation based on contrastive learning processes positive examples with single-grain random masked language model (MLM), but fall short in learn information inside events from multiple aspects. In this paper, we introduce multi-grained contrastive learning and triple-mixture of experts (MCTM) for event representation learning. Our proposed method extends the random MLM by incorporating a specialized MLM designed to capture different grammatical structures within events, which allows the model to learn token-level knowledge from multiple perspectives. Furthermore, we have observed that mask tokens with different granularities affect the model differently, therefore, we incorporate mixture of experts (MoE) to learn importance weights associated with different granularities. Our experiments demonstrate that MCTM outperforms other baselines in tasks such as hard similarity and transitive sentence similarity, highlighting the superiority of our method.