Improving Event Representation via Contrastive Learning and Gaussian Embedding
Guixiang Liao, Liwei Dong, Jinrong Mo, Yanxia Zhou, Yanli Chen · 2024
Event representation in text is basic task for natural language processing. In this paper, an enhanced event representation framework using contrastive learning based on Gaussian embedding (EventGE) is proposed. To make the representation robustly for downstream tasks, Gaussian distribution for each event density embedding is constructed through specific Multi-layer Perceptron (MLP). Also we feed the Gaussian distribution for positive and negative samples to contrastive learning network to gain the accuracy representation by setting the loss function. The experimental results show the accuracy rate is improved significantly compared to the current state-of-the-art methods with some public datasets.