Fin-Fact: A Benchmark Dataset for Multimodal Financial Fact Checking and Explanation Generation
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| Main Authors: | , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866911861930196992 |
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| author | Rangapur, Aman Wang, Haoran Jian, Ling Shu, Kai |
| author_facet | Rangapur, Aman Wang, Haoran Jian, Ling Shu, Kai |
| contents | Fact-checking in financial domain is under explored, and there is a shortage of quality dataset in this domain. In this paper, we propose Fin-Fact, a benchmark dataset for multimodal fact-checking within the financial domain. Notably, it includes professional fact-checker annotations and justifications, providing expertise and credibility. With its multimodal nature encompassing both textual and visual content, Fin-Fact provides complementary information sources to enhance factuality analysis. Its primary objective is combating misinformation in finance, fostering transparency, and building trust in financial reporting and news dissemination. By offering insightful explanations, Fin-Fact empowers users, including domain experts and end-users, to understand the reasoning behind fact-checking decisions, validating claim credibility, and fostering trust in the fact-checking process. The Fin-Fact dataset, along with our experimental codes is available at https://github.com/IIT-DM/Fin-Fact/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_08793 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Fin-Fact: A Benchmark Dataset for Multimodal Financial Fact Checking and Explanation Generation Rangapur, Aman Wang, Haoran Jian, Ling Shu, Kai Artificial Intelligence Computational Engineering, Finance, and Science Machine Learning I.2; E.m Fact-checking in financial domain is under explored, and there is a shortage of quality dataset in this domain. In this paper, we propose Fin-Fact, a benchmark dataset for multimodal fact-checking within the financial domain. Notably, it includes professional fact-checker annotations and justifications, providing expertise and credibility. With its multimodal nature encompassing both textual and visual content, Fin-Fact provides complementary information sources to enhance factuality analysis. Its primary objective is combating misinformation in finance, fostering transparency, and building trust in financial reporting and news dissemination. By offering insightful explanations, Fin-Fact empowers users, including domain experts and end-users, to understand the reasoning behind fact-checking decisions, validating claim credibility, and fostering trust in the fact-checking process. The Fin-Fact dataset, along with our experimental codes is available at https://github.com/IIT-DM/Fin-Fact/. |
| title | Fin-Fact: A Benchmark Dataset for Multimodal Financial Fact Checking and Explanation Generation |
| topic | Artificial Intelligence Computational Engineering, Finance, and Science Machine Learning I.2; E.m |
| url | https://arxiv.org/abs/2309.08793 |