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End-to-End Multimodal Fact-Checking and Explanation Generation: A Challenging Dataset and Models (2205.12487v2)

Published 25 May 2022 in cs.CL

Abstract: We propose 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 evidence and predicting a truthfulness label (e.g., support, refute or not enough information), and to generate a statement to summarize and explain the reasoning and ruling process. To support this research, we construct Mocheg, a large-scale dataset consisting of 15,601 claims where each claim is annotated with a truthfulness label and a ruling statement, and 33,880 textual paragraphs and 12,112 images in total as evidence. To establish baseline performances on Mocheg, we experiment with several 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-end multimodal fact-checking does not provide satisfactory outcomes. To the best of our knowledge, we are the first to build the benchmark dataset and solutions for end-to-end multimodal fact-checking and explanation generation. The dataset, source code and model checkpoints are available at https://github.com/VT-NLP/Mocheg.

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Authors (5)
  1. Barry Menglong Yao (5 papers)
  2. Aditya Shah (9 papers)
  3. Lichao Sun (186 papers)
  4. Jin-Hee Cho (43 papers)
  5. Lifu Huang (92 papers)
Citations (62)
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GitHub

  1. GitHub - VT-NLP/Mocheg: Dataset and Code for Multimodal Fact Checking and Explanation Generation (Mocheg) (35 stars)