GenEARL: A Training-Free Generative Framework for Multimodal Event Argument Role Labeling

• URL: http://arxiv.org/abs/2404.04763v1
• Date: Sun, 7 Apr 2024 00:28:13 GMT
• Title: GenEARL: A Training-Free Generative Framework for Multimodal Event Argument Role Labeling
• Authors: Hritik Bansal, Po-Nien Kung, P. Jeffrey Brantingham, Kai-Wei Chang, Nanyun Peng,
• Abstract summary: GenEARL is a training-free generative framework that harnesses the power of modern generative models to understand event task descriptions. We show that GenEARL outperforms the contrastive pretraining (CLIP) baseline by 9.4% and 14.2% accuracy for zero-shot EARL on the M2E2 and SwiG datasets.
• Score: 89.07386210297373
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal event argument role labeling (EARL), a task that assigns a role for each event participant (object) in an image is a complex challenge. It requires reasoning over the entire image, the depicted event, and the interactions between various objects participating in the event. Existing models heavily rely on high-quality event-annotated training data to understand the event semantics and structures, and they fail to generalize to new event types and domains. In this paper, we propose GenEARL, a training-free generative framework that harness the power of the modern generative models to understand event task descriptions given image contexts to perform the EARL task. Specifically, GenEARL comprises two stages of generative prompting with a frozen vision-language model (VLM) and a frozen large language model (LLM). First, a generative VLM learns the semantics of the event argument roles and generates event-centric object descriptions based on the image. Subsequently, a LLM is prompted with the generated object descriptions with a predefined template for EARL (i.e., assign an object with an event argument role). We show that GenEARL outperforms the contrastive pretraining (CLIP) baseline by 9.4% and 14.2% accuracy for zero-shot EARL on the M2E2 and SwiG datasets, respectively. In addition, we outperform CLIP-Event by 22% precision on M2E2 dataset. The framework also allows flexible adaptation and generalization to unseen domains.

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