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

• URL: http://arxiv.org/abs/2205.12487v2
• Date: Thu, 6 Jul 2023 21:22:45 GMT
• Title: End-to-End Multimodal Fact-Checking and Explanation Generation: A Challenging Dataset and Models
• Authors: Barry Menglong Yao (1), Aditya Shah (1), Lichao Sun (2), Jin-Hee Cho (1), Lifu Huang (1) ((1) Virginia Tech, (2) Lehigh University)
• Abstract summary: We propose end-to-end multimodal fact-checking and explanation generation. The goal is to assess the truthfulness of a claim by retrieving relevant evidence and predicting a truthfulness label. To support this research, we construct Mocheg, a large-scale dataset consisting of 15,601 claims.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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