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.

SemViQA-TC is one of the key components of the SemViQA system, designed for three-class classification in Vietnamese fact-checking. This model classifies a given claim into one of three categories: SUPPORTED, REFUTED, or NOT ENOUGH INFORMATION (NEI) based on retrieved evidence. To address these challenges, SemViQA integrates:

Semantic-based Evidence Retrieval (SER): Combines TF-IDF with a Question Answering Token Classifier (QATC) to enhance retrieval precision while reducing inference time.
Two-step Verdict Classification (TVC): Uses hierarchical classification optimized with Cross-Entropy and Focal Loss, improving claim verification across three categories:
- Supported ✅
- Refuted ❌
- Not Enough Information (NEI) 🤷‍♂️

Model Information

Developed by: SemViQA Research Team
Fine-tuned model: InfoXLM
Supported Language: Vietnamese
Task: Three-Class Classification (Fact Verification)
Dataset: ViWikiFC

SemViQA-TC serves as the first step in the two-step classification process of the SemViQA system. It initially categorizes claims into three classes: SUPPORTED, REFUTED, or NEI. For claims classified as SUPPORTED or REFUTED, a secondary binary classification model (SemViQA-BC) further refines the prediction. This hierarchical classification strategy enhances the accuracy of fact verification.

🏆 Achievements

1st place in the UIT Data Science Challenge 🏅
State-of-the-art performance on:
- ISE-DSC01 → 78.97% strict accuracy
- ViWikiFC → 80.82% strict accuracy
SemViQA Faster: 7x speed improvement over the standard model 🚀

These results establish SemViQA as a benchmark for Vietnamese fact verification, advancing efforts to combat misinformation and ensure information integrity.

Usage Example

Direct Model Usage

Python

# Install semviqa
!pip install semviqa

# Initalize a pipeline
import torch
import torch.nn.functional as F
from transformers import AutoTokenizer
from semviqa.tvc.model import ClaimModelForClassification

tokenizer = AutoTokenizer.from_pretrained("SemViQA/tc-infoxlm-viwikifc")
model = ClaimModelForClassification.from_pretrained("SemViQA/tc-infoxlm-viwikifc")
claim = "Chiến tranh với Campuchia đã kết thúc trước khi Việt Nam thống nhất."
evidence = "Sau khi thống nhất, Việt Nam tiếp tục gặp khó khăn do sự sụp đổ và tan rã của đồng minh Liên Xô cùng Khối phía Đông, các lệnh cấm vận của Hoa Kỳ, chiến tranh với Campuchia, biên giới giáp Trung Quốc và hậu quả của chính sách bao cấp sau nhiều năm áp dụng."

inputs = tokenizer(
    claim,
    evidence,
    truncation="only_second",
    add_special_tokens=True,
    max_length=256,
    padding='max_length',
    return_attention_mask=True,
    return_token_type_ids=False,
    return_tensors='pt',
)

labels = ["NEI", "SUPPORTED", "REFUTED"]

with torch.no_grad():
    outputs = model(**inputs)

logits = outputs["logits"]
probabilities = F.softmax(logits, dim=1).squeeze()

for i, (label, prob) in enumerate(zip(labels, probabilities.tolist()), start=1):
    print(f"{i}) {label} {prob:.4f}")
# 1) NEI 0.0001
# 2) SUPPORTED 0.0001
# 3) REFUTED 0.9998

Evaluation Results

SemViQA-TC is one of the key components of the two-step classification (TVC) approach in the SemViQA system. SemViQA-TC achieved impressive results on the test set, demonstrating accurate and efficient classification capabilities. The detailed evaluation of SemViQA-TC is presented in the table below.

Method	ViWikiFC
ER	VC	Strict Acc	VC Acc	ER Acc	Time (s)
TF-IDF	InfoXLM_large	75.56	82.21	90.15	131
XLM-R_large	76.47	82.78	90.15	134
Ernie-M_large	75.56	81.83	90.15	144
BM25	InfoXLM_large	70.44	79.01	83.50	130
XLM-R_large	70.97	78.91	83.50	132
Ernie-M_large	70.21	78.29	83.50	141
SBert	InfoXLM_large	74.99	81.59	89.72	195
XLM-R_large	75.80	82.35	89.72	194
Ernie-M_large	75.13	81.44	89.72	203
QA-based approaches	VC
ViMRC_large	InfoXLM_large	77.28	81.97	92.49	3778
XLM-R_large	78.29	82.83	92.49	3824
Ernie-M_large	77.38	81.92	92.49	3785
InfoXLM_large	InfoXLM_large	78.14	82.07	93.45	4092
XLM-R_large	79.20	83.07	93.45	4096
Ernie-M_large	78.24	82.21	93.45	4102
LLM
Qwen2.5-1.5B-Instruct	51.03	65.18	78.96	7665
Qwen2.5-3B-Instruct	44.38	62.31	71.35	12123
LLM	VC
Qwen2.5-1.5B-Instruct	InfoXLM_large	66.14	76.47	78.96	7788
XLM-R_large	67.67	78.10	78.96	7789
Ernie-M_large	66.52	76.52	78.96	7794
Qwen2.5-3B-Instruct	InfoXLM_large	59.88	72.50	71.35	12246
XLM-R_large	60.74	73.08	71.35	12246
Ernie-M_large	60.02	72.21	71.35	12251
SER Faster (ours)	TVC (ours)
TF-IDF + ViMRC_large	Ernie-M_large	79.44	82.93	94.60	410
TF-IDF + InfoXLM_large	Ernie-M_large	79.77	83.07	95.03	487
SER (ours)	TVC (ours)
TF-IDF + ViMRC_large	InfoXLM_large	80.25	83.84	94.69	2731
XLM-R_large	80.34	83.64	94.69	2733
Ernie-M_large	79.53	82.97	94.69	2733
TF-IDF + InfoXLM_large	InfoXLM_large	80.68	83.98	95.31	3860
XLM-R_large	80.82	83.88	95.31	3843
Ernie-M_large	80.06	83.17	95.31	3891

Citation

If you use SemViQA-TC in your research, please cite:

bibtex

@misc{tran2025semviqasemanticquestionanswering,
      title={SemViQA: A Semantic Question Answering System for Vietnamese Information Fact-Checking}, 
      author={Dien X. Tran and Nam V. Nguyen and Thanh T. Tran and Anh T. Hoang and Tai V. Duong and Di T. Le and Phuc-Lu Le},
      year={2025},
      eprint={2503.00955},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2503.00955}, 
}

🔗 Paper Link: SemViQA on arXiv
🔗 Source Code: GitHub - SemViQA

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