Paper Info
Title
Improving Event Representation via Simultaneous Weakly Supervised Contrastive Learning and Clustering
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 prototypebased clustering method demonstrates that the learned prototype vectors are able to implicitly capture various relations between events.
Year
DOI
Venue
2022
10.18653/v1/2022.acl-long.216
PROCEEDINGS OF THE 60TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2022), VOL 1: (LONG PAPERS)
DocType
Volume
Citations 
Conference
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Jun Gao100.34
W. Wang28912.05
Changlong Yu3162.75
Huan Zhao400.68
Wilfred Ng500.34
Xu Ruifeng643253.04