README.md

---
configs:
- config_name: claim
  data_files:
  - split: train
    path: data/claim_train.jsonl
  - split: validation
    path: data/claim_dev.jsonl
  - split: test
    path: data/claim_test.jsonl
- config_name: subclaim
  data_files:
  - split: train
    path: data/subclaim_train.jsonl
  - split: validation
    path: data/subclaim_dev.jsonl
  - split: test
    path: data/subclaim_test.jsonl
---

# wice

WiCE: Real-World Entailment for Claims in Wikipedia

## Dataset Details

### Dataset Description

WiCE is a fine-grained textual entailment dataset built on natural claim and evidence pairs extracted from Wikipedia. Given a sentence in Wikipedia and the corresponding article(s) it cites, we annotate the entailment label, a list of sentences in the cited article(s) that support the claim sentence, and tokens in the claim that are unsupported by the article(s).

This is the `entailment_retrieval` subset that includes the WiCE dataset for entailment and retrieval task. `claim` includes data with the original claims and `subclaim` includes data with the decomposed claims (finegrained annotation by using Claim-Split).

### Dataset Sources

<!-- Provide the basic links for the dataset. -->

- **Repository:** https://github.com/ryokamoi/wice
- **Paper:** Kamoi, Goyal, Rodriguez, and Durett(2023) [WiCE: Real-World Entailment for Claims in Wikipedia
](https://arxiv.org/abs/2303.01432).

## License

See [LICENSE.md](https://huggingface.co/datasets/jon-tow/wice/blob/main/LICENSE.md) for details.

## Citation

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

```bibtex
@inproceedings{kamoi-etal-2023-wice,
    title = "{W}i{CE}: Real-World Entailment for Claims in {W}ikipedia",
    author = "Kamoi, Ryo  and
      Goyal, Tanya  and
      Rodriguez, Juan  and
      Durrett, Greg",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.emnlp-main.470",
    pages = "7561--7583",
}
```