In this paper, we address the poor generalization of few-shot learning models for event detection (ED) using transfer learning and representation regularization. In particular, we propose to transfer knowledge from open-domain word sense disambiguation into few-shot learning models for ED to improve their generalization to new event types. We also propose a novel training signal derived from dependency graphs to regularize the representation learning for ED. Moreover, we evaluate few-shot learning models for ED with a large-scale human-annotated ED dataset to obtain more reliable insights for this problem. Our comprehensive experiments demonstrate that the proposed model outperforms state-of-the-art baseline models in the few-shot learning and supervised learning settings for ED.