End-to-End Multimodal Fact-Checking and Explanation Generation: A Challenging Dataset and Models

<p>We propose the end-to-end multimodal fact-checking and explanation generation, where the input is a claim and a large collection of web sources, including articles, images, videos, and tweets, and the goal is to assess the truthfulness of the claim by retrieving relevant evidences and predicting a truthfulness label (i.e., support, refute and not enough information), and to generate a rationalization statement to explain the reasoning and ruling process. To support this research, we construct MOCHEG, a large-scale dataset consisting of 21,184 &nbsp;claims where each claim is annotated with a truthfulness label and ruling statement, with 43,148 text evidences and 15,373 image evidences. &nbsp;</p>