CFEVER-data

Introduction to CFEVER

This repository contains the dataset for our AAAI 2024 paper, "CFEVER: A Chinese Fact Extraction and VERification Dataset". Paper link.

Leaderboard website

Please visit https://ikmlab.github.io/CFEVER to check the leaderboard of CFEVER.

How to load CFEVER

from datasets import load_dataset

# Get ready data ["train", "dev", "test"]
ds = load_dataset("IKMLab-team/cfever")

# Get separted data by:
train, dev, test = ds["train"], ds["dev"], ds["test"]

# If you only want a part of data
train = load_dataset("IKMLab-team/cfever", name="train")["train"]
# or
train = load_dataset("IKMLab-team/cfever", split="train")

# wiki page data:
wiki_pages = load_dataset("IKMLab-team/cfever", name="wiki_pages")["plain"]

Repository structure

CFEVER-data
├── dev.jsonl # CFEVER development set
├── test.jsonl # CFEVER test set without labels and evidence
├── train.jsonl # CFEVER training set
├── wiki*.jsonl # CFEVER wiki pages
├── LICENSE
├── README.md
└── sample_submission.jsonl # sample submission file of the test set

Evaluation

• Please refer to our codebase: https://github.com/IKMLab/CFEVER-baselines/?tab=readme-ov-file#evaluations

Submission

• Please include three fields (necessary) in the prediction file for each claim in the test set.
  • id
  • predicted_label
  • predicted_evidence
• The id field has been already included in the test set. Please do not change the order.
• The predicted_label should be one of supports, refutes, or NOT ENOUGH INFO.
• The predicted_evidence should be a list of evidence sentences, where each evidence sentence is represented by a list of [page_id, line_number]. For example:

# One evidence sentence for the claim
{
    "id": 1,
    "predicted_label": "REFUTES",
    "predicted_evidence": [
        ["page_id_2", 2],
    ]
}

# Two evidence sentences for the claim
{
    "id": 1,
    "predicted_label": "SUPPORTS",
    "predicted_evidence": [
        ["page_id_1", 1],
        ["page_id_2", 2],
    ]
}

# The claim cannot be verified
{
    "id": 1,
    "predicted_label": "NOT ENOUGH INFO",
    "predicted_evidence": None
}

• After creating the prediction file, please email the file to yingjia.lin.public@gmail.com with a brief description of your method. We will evaluate your submission and update the leaderboard.
• A randomly generated submission file can be found here.
  • Note that claim is not necessary to be included in the submission file.
• You can also check the prediction example for the development set and follow the evaluation steps from our CFEVER-baselines repo.

Licensing Information

CFEVER's data annotations incorporate content from Wikipedia, which is licensed under the Wikipedia Copyright Policy. Users of this dataset are responsible for ensuring that their use, redistribution, and downstream applications comply with all applicable licenses and attribution requirements of the Wikipedia license terms.

Reference

If you find our work useful, please cite our paper.

@article{Lin_Lin_Yeh_Li_Hu_Hsu_Lee_Kao_2024,
    title = {CFEVER: A Chinese Fact Extraction and VERification Dataset},
    author = {Lin, Ying-Jia and Lin, Chun-Yi and Yeh, Chia-Jen and Li, Yi-Ting and Hu, Yun-Yu and Hsu, Chih-Hao and Lee, Mei-Feng and Kao, Hung-Yu},
    doi = {10.1609/aaai.v38i17.29825},
    journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
    month = {Mar.},
    number = {17},
    pages = {18626-18634},
    url = {https://ojs.aaai.org/index.php/AAAI/article/view/29825},
    volume = {38},
    year = {2024},
    bdsk-url-1 = {https://ojs.aaai.org/index.php/AAAI/article/view/29825},
    bdsk-url-2 = {https://doi.org/10.1609/aaai.v38i17.29825}
}