UTDRM: Unsupervised Method for Training Debunked-Narrative Retrieval Models

This repository contains the models and code for UTDRM, an unsupervised method for training debunked-narrative retrieval models. The approach involves generating topical claims using T5 and ChatGPT claim generators and training the dense retrieval model using MNR loss.

Usage Instructions:

1. Generate Claims:

   • Utilize the T5 and ChatGPT claim generators located in the generators/ directory to generate claims.

2. Convert Dataset and Mine Hard Negatives:

   • Convert the dataset into BIER format and mine hard negatives using the scripts available at GPL. This process will generate the hard-negatives.jsonl file.

3. Train the Model:

   • Find the script for training the dense retrieval model using MNR loss in the training/ directory.

Best Performing Models:

The best performing models can be accessed via the following Hugging Face model repositories:

• UTDRM-MPNet
• UTDRM-RoBERTa

Citation:

If you use the code or models from this repository, please consider citing the following paper:

@article{singh2023utdrm,
  title={UTDRM: unsupervised method for training debunked-narrative retrieval models},
  author={Singh, Iknoor and Scarton, Carolina and Bontcheva, Kalina},
  journal={EPJ Data Science},
  volume={12},
  number={1},
  pages={59},
  year={2023},
  publisher={Springer}
}

Feel free to reach out if you have any questions or need further assistance.