@inproceedings{gao-etal-2022-improving,
title = "Improving Event Representation via Simultaneous Weakly Supervised Contrastive Learning and Clustering",
author = "Gao, Jun and
Wang, Wei and
Yu, Changlong and
Zhao, Huan and
Ng, Wilfred and
Xu, Ruifeng",
editor = "Muresan, Smaranda and
Nakov, Preslav and
Villavicencio, Aline",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-long.216",
doi = "10.18653/v1/2022.acl-long.216",
pages = "3036--3049",
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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%0 Conference Proceedings
%T Improving Event Representation via Simultaneous Weakly Supervised Contrastive Learning and Clustering
%A Gao, Jun
%A Wang, Wei
%A Yu, Changlong
%A Zhao, Huan
%A Ng, Wilfred
%A Xu, Ruifeng
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F gao-etal-2022-improving
%X 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.
%R 10.18653/v1/2022.acl-long.216
%U https://aclanthology.org/2022.acl-long.216
%U https://doi.org/10.18653/v1/2022.acl-long.216
%P 3036-3049
Markdown (Informal)
[Improving Event Representation via Simultaneous Weakly Supervised Contrastive Learning and Clustering](https://aclanthology.org/2022.acl-long.216) (Gao et al., ACL 2022)
ACL