LOREN is an interpretable fact verification model trained on [FEVER](https://fever.ai), which aims to predict the veracity of a textual claim against a trustworthy knowledge source such as Wikipedia. 
LOREN also decomposes the verification and makes accurate and faithful phrase-level veracity predictions without any phrasal veracity supervision.

This repo hosts the pre-trained fact verification model for LOREN based on RoBERTa (large). More technical details can be found at [this GitHub Repo](https://github.com/jiangjiechen/LOREN).

Please check out our AAAI 2022 paper for more details: "[LOREN: Logic-Regularized Reasoning for Interpretable Fact Verification](https://arxiv.org/abs/2012.13577)".