File size: 4,740 Bytes
60f23ad a9f4627 60f23ad a5879b9 1f8fa4b fd316cc a5879b9 d45ec0a a5879b9 c351084 a5879b9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 | ---
license: apache-2.0
configs:
- config_name: default
data_files:
- split: train
path: train*.jsonl
- split: dev
path: dev*.jsonl
- split: test
path: test*.jsonl
- config_name: train
data_files:
- split: train
path: train*.jsonl
- config_name: dev
data_files:
- split: dev
path: dev*.jsonl
- config_name: test
data_files:
- split: test
path: test*.jsonl
- config_name: wiki_pages
data_files:
- split: plain
path: wiki*.jsonl
---
# 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](https://doi.org/10.1609/aaai.v38i17.29825).
## Leaderboard website
Please visit https://ikmlab.github.io/CFEVER to check the leaderboard of CFEVER.
## How to load CFEVER
```python
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](data/test.jsonl). 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](sample_submission.jsonl).
- Note that `claim` is not necessary to be included in the submission file.
- You can also check [the prediction example for the development set](https://github.com/IKMLab/CFEVER-baselines/blob/main/simple_baseline/data/dumb_dev_pred.jsonl) and follow [the evaluation steps](https://github.com/IKMLab/CFEVER-baselines/tree/main?tab=readme-ov-file#sentence-retrieval-and-claim-verification) from [our CFEVER-baselines repo](https://github.com/IKMLab/CFEVER-baselines).
## 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}
}
```
|