`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)".
{}
arxiv:2012.13577 region:us
TAGS
#arxiv-2012.13577 #region-us 

'LOREN' is an interpretable fact verification model trained on FEVER, 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.

More technical details can be found at this GitHub Repo.

Please check out our AAAI 2022 paper for more details: "LOREN: Logic-Regularized Reasoning for Interpretable Fact Verification".
TAGS
#arxiv-2012.13577 #region-us 

passage: TAGS
#arxiv-2012.13577 #region-us 
