Reading and Reasoning over Chart Images for Evidence-based Automated Fact-Checking

Abstract

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.

QACheck: A Demonstration System for Question-Guided Multi-Hop Fact-Checking

“…In the future, we plan to enhance QACHECK 1) by integrating additional knowledge bases to further improve the breadth and depth of information accessible to the system (Feng et al, 2023;Kim et al, 2023), and 2) by incorporating a multimodal interface to support image (Chakraborty et al, 2023), table (Chen et al, 2020;, and chart-based fact-checking (Akhtar et al, 2023), which can broaden the system's utility in processing and analyzing different forms of data.…”