Improving Event Representation via Simultaneous Weakly Supervised Contrastive Learning and Clustering

• URL: http://arxiv.org/abs/2203.07633v1
• Date: Tue, 15 Mar 2022 04:12:00 GMT
• Title: Improving Event Representation via Simultaneous Weakly Supervised Contrastive Learning and Clustering
• Authors: Jun Gao, Wei Wang, Changlong Yu, Huan Zhao, Wilfred Ng, Ruifeng Xu
• Abstract summary: We present SWCC: a Simultaneous Weakly supervised Contrastive learning and Clustering framework for event representation learning. For model training, SWCC learns representations by simultaneously performing weakly supervised contrastive learning and prototype-based clustering.
• Score: 31.841780703374955
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Representations of events described in text are important for various tasks. In this work, we present SWCC: a Simultaneous Weakly supervised Contrastive learning and Clustering framework for event representation learning. SWCC learns event representations by making better use of co-occurrence information of events. Specifically, we introduce a weakly supervised contrastive learning method that allows us to consider multiple positives and multiple negatives, and a prototype-based clustering method that avoids semantically related events being pulled apart. For model training, SWCC learns representations by simultaneously performing weakly supervised contrastive learning and prototype-based clustering. Experimental results show that SWCC outperforms other baselines on Hard Similarity and Transitive Sentence Similarity tasks. In addition, a thorough analysis of the prototype-based clustering method demonstrates that the learned prototype vectors are able to implicitly capture various relations between events.

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