Textual entailment models are increasingly applied in settings like fact-checking, presupposition verification in question answering, or summary evaluation. However, these represent a significant domain shift from existing entailment datasets, and models underperform as a result. We propose WiCE, a new fine-grained textual entailment dataset built on natural claim and evidence pairs extracted from Wikipedia. In addition to standard claim-level entailment, WiCE provides entailment judgments over sub-sentence units of the claim, and a minimal subset of evidence sentences that support each subclaim. To support this, we propose an automatic claim decomposition strategy using GPT-3.5 which we show is also effective at improving entailment models' performance on multiple datasets at test time. Finally, we show that real claims in our dataset involve challenging verification and retrieval problems that existing models fail to address.
@article{arxiv.2303.01432,
title = {WiCE: Real-World Entailment for Claims in Wikipedia},
author = {Ryo Kamoi and Tanya Goyal and Juan Diego Rodriguez and Greg Durrett},
journal= {arXiv preprint arXiv:2303.01432},
year = {2023}
}