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We propose end-to-end multimodal fact-checking and explanation generation,where the input is a claim and a large collection of web sources, includingarticles, images, videos, and tweets, and the goal is to assess thetruthfulness of the claim by retrieving relevant evidence and predicting atruthfulness label (e.g., support, refute or not enough information), and togenerate a statement to summarize and explain the reasoning and ruling process.To support this research, we construct Mocheg, a large-scale dataset consistingof 15,601 claims where each claim is annotated with a truthfulness label and aruling statement, and 33,880 textual paragraphs and 12,112 images in total asevidence. To establish baseline performances on Mocheg, we experiment withseveral state-of-the-art neural architectures on the three pipelined subtasks:multimodal evidence retrieval, claim verification, and explanation generation,and demonstrate that the performance of the state-of-the-art end-to-endmultimodal fact-checking does not provide satisfactory outcomes. To the best ofour knowledge, we are the first to build the benchmark dataset and solutionsfor end-to-end multimodal fact-checking and explanation generation. Thedataset, source code and model checkpoints are available athttps://github.com/VT-NLP/Mocheg.

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