Advancing Vietnamese Fact Extraction and Verification through Multi-Stage Text Ranking

In digital age, the widespread accessibility of information dissemination has inadvertently accelerated the spread of misinformation, highlighting the critical necessity for robust factchecking systems, particularly for low-resourced languages. Our study propose the ViNSV system, a pioneering approach tailored for the Vietnamese language, utilizing the ISE-DSC01 dataset. It employs a novel multi-stage text ranking method, integrating advanced natural language processing techniques such as BM25, Sentence-BERT for in-depth semantic analysis, and XLM-R for comprehensive factual verification. Our extensive evaluation reveals that ViNSV achieves a breakthrough in accuracy, with a Strict Accuracy of 76.33%, thereby setting a promising benchmark in the field of Vietnamese Fact Extraction and Verification. The ViNSV system not only enhances the reliability of digital content but also offers a scalable model for adaptation to other languages with similar challenges, representing a significant contribution to ensuring global information integrity.