RU22Fact: Optimizing Evidence for Multilingual Explainable Fact-Checking on Russia-Ukraine Conflict

• URL: http://arxiv.org/abs/2403.16662v2
• Date: Tue, 26 Mar 2024 07:13:15 GMT
• Title: RU22Fact: Optimizing Evidence for Multilingual Explainable Fact-Checking on Russia-Ukraine Conflict
• Authors: Yirong Zeng, Xiao Ding, Yi Zhao, Xiangyu Li, Jie Zhang, Chao Yao, Ting Liu, Bing Qin,
• Abstract summary: High-quality evidence plays a vital role in enhancing fact-checking systems. We propose a method based on a Large Language Model to automatically retrieve and summarize evidence from the Web. We construct RU22Fact, a novel explainable fact-checking dataset on the Russia-Ukraine conflict in 2022 of 16K samples.
• Score: 34.2739191920746
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fact-checking is the task of verifying the factuality of a given claim by examining the available evidence. High-quality evidence plays a vital role in enhancing fact-checking systems and facilitating the generation of explanations that are understandable to humans. However, the provision of both sufficient and relevant evidence for explainable fact-checking systems poses a challenge. To tackle this challenge, we propose a method based on a Large Language Model to automatically retrieve and summarize evidence from the Web. Furthermore, we construct RU22Fact, a novel multilingual explainable fact-checking dataset on the Russia-Ukraine conflict in 2022 of 16K samples, each containing real-world claims, optimized evidence, and referenced explanation. To establish a baseline for our dataset, we also develop an end-to-end explainable fact-checking system to verify claims and generate explanations. Experimental results demonstrate the prospect of optimized evidence in increasing fact-checking performance and also indicate the possibility of further progress in the end-to-end claim verification and explanation generation tasks.

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