@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, Dương 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://aclanthology.org/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."
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%0 Conference Proceedings
%T SemViQA: A Semantic Question Answering System for Vietnamese Information Fact-Checking
%A Tran, Dien X.
%A Nguyen, Nam V.
%A Thanh, Tran Tan
%A Hoang, Anh T.
%A Tài, Dương Văn
%A Di, Lê Thanh
%A Le, Phuc-Lu
%Y Li, Yunyao
%Y Rehm, Georg
%Y Tu, Mei
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-394-4
%F tran-etal-2026-semviqa
%X 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× while maintaining competitive accuracy. SemViQA sets a new benchmark for Vietnamese fact verification, advancing the fight against misinformation.
%U https://aclanthology.org/2026.acl-industry.94/
%P 1341-1358
Markdown (Informal)
[SemViQA: A Semantic Question Answering System for Vietnamese Information Fact-Checking](https://aclanthology.org/2026.acl-industry.94/) (Tran et al., ACL 2026)
ACL
- Dien X. Tran, Nam V. Nguyen, Tran Tan Thanh, Anh T. Hoang, Dương Văn Tài, Lê Thanh Di, and Phuc-Lu Le. 2026. SemViQA: A Semantic Question Answering System for Vietnamese Information Fact-Checking. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), pages 1341–1358, San Diego, California, USA. Association for Computational Linguistics.