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Contemporary news reporting increasingly features multimedia content,motivating research on multimedia event extraction. However, the task lacksannotated multimodal training data and artificially generated training datasuffer from distribution shift from real-world data. In this paper, we proposeCross-modality Augmented Multimedia Event Learning (CAMEL), which successfullyutilizes artificially generated multimodal training data and achievesstate-of-the-art performance. We start with two labeled unimodal datasets intext and image respectively, and generate the missing modality usingoff-the-shelf image generators like Stable Diffusion and image captioners likeBLIP. After that, we train the network on the resultant multimodal datasets. Inorder to learn robust features that are effective across domains, we devise aniterative and gradual training strategy. Substantial experiments show thatCAMEL surpasses state-of-the-art (SOTA) baselines on the M2E2 benchmark. Onmultimedia events in particular, we outperform the prior SOTA by 4.2% F1 onevent mention identification and by 9.8% F1 on argument identification, whichindicates that CAMEL learns synergistic representations from the twomodalities. Our work demonstrates a recipe to unleash the power of synthetictraining data in structured prediction.

Training Multimedia Event Extraction With Generated Images and Captions