Datasets:

High-Will
/

ViWikiFC

language:
  - vi
license: cc-by-4.0
pretty_name: ViWikiFC
size_categories:
  - 10K<n<100K
task_categories:
  - text-classification
  - text-retrieval
task_ids:
  - fact-checking
  - fact-checking-retrieval
  - natural-language-inference
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train.csv
      - split: dev
        path: data/dev.csv
      - split: test
        path: data/test.csv

ViWikiFC: Vietnamese Wikipedia Fact-Checking Dataset

Dataset Summary

ViWikiFC is the first large-scale open-domain Vietnamese fact-checking dataset constructed from Vietnamese Wikipedia. The dataset is designed to support research on automated fact verification, evidence retrieval, and natural language inference (NLI) for Vietnamese.

The corpus consists of 20,916 manually annotated claim–evidence pairs generated from 3,812 evidence sentences extracted from 73 Vietnamese Wikipedia articles across multiple domains including history, geography, science, culture, philosophy, and society.

Each claim is assigned one of three labels:

SUPPORTED – the evidence supports the claim.
REFUTED – the evidence contradicts the claim.
NOT_ENOUGH_INFORMATION (NEI) – the evidence is insufficient to verify the claim.

Unlike automatically generated datasets, all claims in ViWikiFC were manually written by native Vietnamese annotators, resulting in natural linguistic diversity and realistic reasoning patterns.

Supported Tasks

ViWikiFC can be used for:

Fact Verification
Evidence Retrieval
Natural Language Inference
Retrieval-Augmented Generation (RAG) evaluation
Dense Retrieval
Sparse Retrieval
Cross-Encoder Re-ranking
Vietnamese Language Model Evaluation

Languages

Vietnamese (vi)

Dataset Structure

Data Instances

An example instance is shown below.

{
  "claim": "Việt Nam gia nhập ASEAN vào năm 1995.",
  "evidence": "Việt Nam chính thức trở thành thành viên ASEAN vào ngày 28 tháng 7 năm 1995.",
  "label": "SUPPORTED"
}

Data Fields

Each sample contains the following fields.

Field	Description
`claim`	A manually written Vietnamese claim
`evidence`	Evidence sentence extracted from Wikipedia
`label`	Verification label (`SUPPORTED`, `REFUTED`, `NOT_ENOUGH_INFORMATION`)

Depending on the released version, additional metadata such as article title or evidence identifiers may also be included.

Data Splits

The dataset is officially divided into training, dev, and test sets.

Split	Samples
Train	16,738
Dev	2,090
Test	2,091
Total	20,916

The label distribution is approximately balanced across all splits.

Dataset Creation

Source Data

The evidence sentences were collected from Vietnamese Wikipedia.

73 Wikipedia articles
1,479 paragraphs
3,812 evidence sentences

The selected articles cover multiple knowledge domains, including

History
Geography
Science
Philosophy
Culture
Society

Annotation Process

Thirty native Vietnamese speakers participated in the annotation process.

The annotation pipeline consisted of four stages:

Annotator recruitment and training
Evidence selection
Claim generation
Corpus dev

For every evidence sentence, annotators manually generated claims for three verification labels:

SUPPORTED
REFUTED
NOT_ENOUGH_INFORMATION

To improve linguistic diversity, annotators were encouraged to use various rewriting strategies, including

synonym substitution
paraphrasing
syntactic restructuring
active/passive transformation
reasoning modification
presupposition
quantity and temporal changes
negation

Each evidence sentence generated multiple claims with different semantic relationships.

The final corpus achieved a Fleiss' κ agreement of 95.87%, indicating high annotation consistency.

Dataset Statistics

Property	Value
Evidence sentences	3,812
Wikipedia articles	73
Claim–evidence pairs	20,916
Annotators	30
Labels	3
Fleiss' κ	95.87%

Benchmark Results

The dataset provides two benchmark tasks.

Evidence Retrieval

Method	Top-1 Accuracy
TF-IDF	51.32%
SBERT	65.12%
BM25	77.27%

Verdict Prediction

The strongest baseline model is InfoXLM-Large, achieving

Accuracy: 86.50%
Macro F1: 86.51%

End-to-End Pipeline

The best pipeline combines

BM25
InfoXLM-Large

with a Strict Accuracy of 67.00%, demonstrating that Vietnamese fact-checking remains a challenging task.

Intended Uses

ViWikiFC is intended for research purposes, including

Automated fact-checking
Evidence retrieval
Natural language inference
Retrieval-Augmented Generation
Vietnamese LLM evaluation
Information verification
Benchmarking retrieval models

Limitations

Although the dataset covers multiple domains, it has several limitations.

Evidence is restricted to Vietnamese Wikipedia.
Claims are generated from single evidence sentences.
The corpus does not include multimodal evidence such as tables or images.
The dataset focuses on open-domain factual verification and does not cover social media misinformation.

Citation

If you use ViWikiFC in your work, please cite:

@inproceedings{le-etal-2026-viwikifc,
    title = "{V}i{W}iki{FC}: Fact-Checking for {V}ietnamese {W}ikipedia-Based Textual Knowledge Source",
    author = "Le, Hung Tuan  and
      To, Long Truong  and
      Nguyen, Manh Trong  and
      Nguyen, Kiet Van",
    editor = "Piperidis, Stelios  and
      Bel, N{\'u}ria  and
      van den Heuvel, Henk  and
      Ide, Nancy  and
      Krek, Simon  and
      Toral, Antonio",
    booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resource Association",
    url = "https://aclanthology.org/2026.lrec-1.869/",
    doi = "10.63317/5ew98tfds6jc",
    pages = "11125--11140",
    abstract = "Fact-checking is essential due to the explosion of misinformation in the media ecosystem. Although false information exists in every language and country, most research to solve the problem has mainly concentrated on huge communities like English and Chinese. Low-resource languages like Vietnamese are necessary to explore corpora and models for fact verification. To bridge this gap, we construct ViWikiFC, the first manually annotated open-domain corpus for \textbf{Vi}etnamese \textbf{Wiki}pedia \textbf{F}act \textbf{C}hecking more than 20K claims generated by converting evidence sentences extracted from Wikipedia articles. We analyze our corpus through many linguistic aspects, from the new dependency rate, the new n-gram rate, and the new word rate. We conducted various experiments for Vietnamese fact-checking, including evidence retrieval and verdict prediction. BM25 and InfoXLM$_{Large}$ achieved the best results in two tasks, with BM25 achieving an accuracy of 88.30{\%} for SUPPORTS, 86.93{\%} for REFUTES, and only 56.67{\%} for the NEI label in the evidence retrieval task. InfoXLM$_{Large}$ achieved an F$_1$ score of 86.51{\%}. Furthermore, we also conducted a pipeline approach, which only achieved a strict accuracy of 67.00{\%} when using InfoXLM$_{Large}$ and BM25. These results demonstrate that our dataset is challenging for the Vietnamese language model in fact-checking tasks."
}

License

This dataset is released under the CC BY 4.0 License unless otherwise specified.

Acknowledgements

This research was supported by Vietnam National University Ho Chi Minh City (VNU-HCM) under grant DS2025-26-01.

We sincerely thank all annotators for their valuable contributions to the creation of this corpus.