Zero-shot-Fact-Verification-by-Claim-Generation

This repository contains code and models for the paper: Zero-shot Fact Verification by Claim Generation (ACL-IJCNLP 2021).

• We explore the possibility of automatically generating large-scale (evidence, claim) pairs to train the fact verification model.

• We propose a simple yet general framework Question Answering for Claim Generation (QACG) to generate three types of claims from any given evidence: 1) claims that are supported by the evidence, 2) claims that are refuted by the evidence, and 3) claims that the evidence does Not have Enough Information (NEI) to verify.

• We show that the generated training data can greatly benefit the fact verification system in both zero-shot and few-shot learning settings.

General Framework of QACG

Example of Generated Claims

Requirements

• Python 3.7.3
• torch 1.7.1
• tqdm 4.49.0
• transformers 4.3.3
• stanza 1.1.1
• nltk 3.5
• scikit-learn 0.23.2

Reference

Please cite the paper in the following format if you use this dataset during your research.

@inproceedings{pan-etal-2021-Zero-shot-FV,
  title={Zero-shot Fact Verification by Claim Generation},
  author={Liangming Pan, Wenhu Chen, Wenhan Xiong, Min-Yen Kan, William Yang Wang},
  booktitle = {The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021)},
  address = {Online},
  month = {August},
  year = {2021}
}

Q&A

If you encounter any problem, please either directly contact the first author or leave an issue in the github repo.