| `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 following pre-trained models for `LOREN`: | |
| - `fact_checking/`: the verification models based on BERT (large) and RoBERTa (large), respectively. | |
| - `mrc_seq2seq/`: the generative machine reading comprehension model based on BART (base). | |
| - `evidence_retrieval/`: the evidence sentence ranking models, which are copied directly from [KGAT](https://github.com/thunlp/KernelGAT). | |
| 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)". |