Reading and Reasoning over Chart Images for Evidence-based Automated Fact-Checking
This addresses the need for multimodal evidence in automated fact-checking, focusing on a novel chart-based task, though it is incremental in applying vision-language models to a specific domain.
The paper tackles the problem of automated fact-checking using chart images as evidence, introducing ChartBERT which achieves 63.8% accuracy on a new dataset of 15,886 charts.
Evidence data for automated fact-checking (AFC) can be in multiple modalities such as text, tables, images, audio, or video. While there is increasing interest in using images for AFC, previous works mostly focus on detecting manipulated or fake images. We propose a novel task, chart-based fact-checking, and introduce ChartBERT as the first model for AFC against chart evidence. ChartBERT leverages textual, structural and visual information of charts to determine the veracity of textual claims. For evaluation, we create ChartFC, a new dataset of 15, 886 charts. We systematically evaluate 75 different vision-language (VL) baselines and show that ChartBERT outperforms VL models, achieving 63.8% accuracy. Our results suggest that the task is complex yet feasible, with many challenges ahead.