Contemporary news reporting increasingly features multimedia content,
motivating research on multimedia event extraction. However, the task lacks
annotated multimodal training data and artificially generated training data
suffer from the distribution shift from the real-world data. In this paper, we
propose Cross-modality Augmented Multimedia Event Learning (CAMEL), which
successfully utilizes artificially generated multimodal training data and
achieves state-of-the-art performance. Conditioned on unimodal training data,
we generate multimodal training data using off-the-shelf image generators like
Stable Diffusion and image captioners like BLIP. In order to learn robust
features that are effective across domains, we devise an iterative and gradual
annealing training strategy. Substantial experiments show that CAMEL surpasses
state-of-the-art (SOTA) baselines on the M2E2 benchmark. On multimedia events
in particular, we outperform the prior SOTA by 4.2\% F1 on event mention
identification and by 9.8\% F1 on argument identification, which demonstrates
that CAMEL learns synergistic representations from the two modalities.

本文提出了一个名为 CAMEL 的跨模态增强多媒体事件学习方法（Cross-modality Augmented Multimedia Event Learning），它使用了人工生成的多模态训练数据，实现了领先水平，并在多媒体事件提取方面优于现有研究。