引言

本文贡献

We are motivated to address the above issues with the goal of making better use of cooccurrence information of events.

To this end, we present SWCC: a Simultaneous Weakly supervised Contrastive learning and Clustering framework for event representation learning, where we exploit document-level co-occurrence information of events as weak supervision and learn event representations by simultaneously performing weakly supervised contrastive learning and prototype-based clustering.

To address the first issue, we build our approach on the contrastive framework with the InfoNCE objective (van den Oord et al., 2019), which is a self-supervised contrastive learning method that uses one positive and multiple negatives.

Further, we extend the InfoNCE to a weakly supervised contrastive learning setting, allowing us to consider multiple positives and multiple negatives per anchor (as opposed to the previous works which use only one positive and one negative).

Co-occurring events are then incorporated as additional positives, weighted by a normalized co-occurrence frequency.

To address the second issue, we introduce a prototype-based clustering method to avoid semantically related events being pulled apart.

Specifically, we impose a prototype for each cluster, which is a representative embedding for a group of semantically related events.

Then we cluster the data while enforce consistency between cluster assignments produced for different augmented representations of an event.

Unlike the instance-wise contrastive learning, our clustering method focuses on the cluster-level semantic concepts by contrasting between representations of events and clusters.

% 第五段：贡献总结

Overall, we make the following contributions:

• We propose a simple and effective framework (SWCC) that learns event representations by making better use of co-occurrence information of events. Experimental results show that our approach outperforms previous approaches on several event related tasks.

• 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.

• We provide a thorough analysis of the prototypebased clustering method to demonstrate that the learned prototype vectors are able to implicitly capture various relations between events.