SemViQA: A Semantic Question Answering System for Vietnamese Information
  Fact-Checking
Authors: name: Nam V. Nguyen, name: Dien X. Tran, name: Thanh T. Tran, name: Anh T. Hoang, name: Tai V. Duong, name: Di T. Le, name: Phuc-Lu Le

The rise of misinformation, exacerbated by Large Language Models (LLMs) like
GPT and Gemini, demands robust fact-checking solutions, especially for
low-resource languages like Vietnamese. Existing methods struggle with semantic
ambiguity, homonyms, and complex linguistic structures, often trading accuracy
for efficiency. We introduce SemViQA, a novel Vietnamese fact-checking
framework integrating Semantic-based Evidence Retrieval (SER) and Two-step
Verdict Classification (TVC). Our approach balances precision and speed,
achieving state-of-the-art results with 78.97\% strict accuracy on ISE-DSC01
and 80.82\% on ViWikiFC, securing 1st place in the UIT Data Science Challenge.
Additionally, SemViQA Faster improves inference speed 7x while maintaining
competitive accuracy. SemViQA sets a new benchmark for Vietnamese fact
verification, advancing the fight against misinformation. The source code is
available at: https://github.com/DAVID-NGUYEN-S16/SemViQA.