论文标题
通过同时进行弱监督的对比学习和聚类来改善事件表示
Improving Event Representation via Simultaneous Weakly Supervised Contrastive Learning and Clustering
论文作者
论文摘要
文本中描述的事件的表示对于各种任务很重要。在这项工作中，我们介绍了SWCC：同时对事件表示学习的同时弱监督的对比度学习和聚类框架。 SWCC通过更好地利用事件的共同出现信息来学习事件表示形式。具体来说，我们引入了一种弱监督的对比学习方法，该方法使我们可以考虑多种阳性和多种负面因素以及一种基于原型的聚类方法，该方法避免了将语义相关的事件拉开。对于模型培训，SWCC通过同时执行弱监督的对比度学习和基于原型的聚类来学习表示形式。实验结果表明，SWCC在硬性相似性和及时句子相似性任务方面优于其他基准。此外，对基于原型的聚类方法的彻底分析表明，学到的原型矢量能够隐式捕获事件之间的各种关系。
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.
