Multi-channels Prototype Contrastive Learning with Condition Adversarial Attacks for Few-shot Event Detection

Abstract Few-shot Event Detection (FSED) is a sub-task of Event Detection that aims to accurately identify event types with limited training instances and enable smooth transfer to newly-emerged event types. Recently, the dominant works have used the prototypical network to accomplish this task and employ contrastive learning to alleviate the issue of semantically-close categories. Nevertheless, these methods still suffer from two serious problems: (1) inadequate learning of prototype representations resulting from limited training data; (2) hard-easy sample imbalance and categories imbalance caused by the large number of non-trigger word("O" tags) in the token-level classification task. To address the problems, this paper proposes the Multi-channels Prototype and Contrastive learning method with Conditional Adversarial attack, which introduces the improved multi-channels prototype and contrastive networks to alleviate the categories and hard-easy samples imbalance. Moreover, we devise a constrained adversarial attack to improve the problem of limited training data. Extensive experimental results show that our model performs better than other FSED methods. All the code and data will be available for online public access.