@inproceedings{tran-etal-2026-semviqa,
    title = "{S}em{V}i{QA}: A Semantic Question Answering System for {V}ietnamese Information Fact-Checking",
    author = "Tran, Dien X.  and
      Nguyen, Nam V.  and
      Thanh, Tran Tan  and
      Hoang, Anh T.  and
      T{\`a}i, Duong V{\u{a}}n  and
      Di, L{\^e} Thanh  and
      Le, Phuc-Lu",
    editor = "Li, Yunyao  and
      Rehm, Georg  and
      Tu, Mei",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics ({ACL} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-industry.94/",
    pages = "1341--1358",
    ISBN = "979-8-89176-394-4",
    abstract = "Recent advances in LLMs have accelerated both information generation and misinformation, especially in low-resource languages like Vietnamese, motivating robust fact-checking systems. 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 7{\texttimes} while maintaining competitive accuracy. SemViQA sets a new benchmark for Vietnamese fact verification, advancing the fight against misinformation."
}