Multimedia Event Extraction From News With a Unified Contrastive Learning Framework

Extracting events from news have seen many benefits in downstream applications. Today's event extraction (EE) systems, however, usually focus on a single modality --- either for text or image, and such methods suffer from incomplete information because a news document is typically presented in a multimedia format. In this paper, we propose a new method for multimedia EE by bridging the textual and visual modalities with a unified contrastive learning framework. Our central idea is to create a shared space for texts and images in order to improve their similar representation. This is accomplished by training on text-image pairs in general, and we demonstrate that it is possible to use this framework to boost learning for one modality by investigating the complementary of the other modality. On the benchmark dataset, our approach establishes a new state-of-the-art performance and shows a 3 percent improvement in F1. Furthermore, we demonstrate that it can achieve cutting-edge performance for visual EE even in a zero-shot scenario with no annotated data in the visual modality.