Official Codebase for Unsupervised Explanation Generation via Correct Instantiations.
Please feel free to contact us with any questions or advices: sjcheng20@fudan.edu.cn.
In this paper, We propose a two-phase framework NEON to help large PLMs generate explanations by implicitly identifying conflict points in the statement.
Figure 1: The framework of Neon.
Table 1: Automatic evaluation results.
Table 2: Human evaluation results.
The code is mainly based on PyTorch and Huggingface transformers.
- Python==3.9.10
- pytorch==1.10.2
- transformers==4.21.0
Our main datasets are ComVE and e-SNLI. You need to download them and put them under the data folder. The detailed structure of files can be found in data/ComVE/README.md and data/e-SNLI/README.md, respectively.
In some sections, we conduct simple strategies to process our datasets, such as filtering and shuffling. These scripts are omitted, but you can easily reproduce them by yourself.
Conduct in-context learning in the phase I:
cd ./Phase1/in_context/
sh run_phase1.sh
Conduct CGMH (unsupervised method) in the phase I:
cd ./Phase1/CGMH/
sh run.sh
cd ./Phase2/
sh run_phase2.sh
cd ./Evaluation/
sh run_eval.sh
We put the python-script XXX.py and its corresponding sh-script XXX.sh in the XXX section. You can directly run the XXX.sh for each analysis experiment to validate. We take the first analysis experiment Quality as an example:
cd ./Analysis/Quality/
sh run_binary.sh
Please add this citation if our paper or code helps you :)
@article{cheng2022unsupervised,
title={Unsupervised Explanation Generation via Correct Instantiations},
author={Cheng, Sijie and Wu, Zhiyong and Chen, Jiangjie and Li, Zhixing and Liu, Yang and Kong, Lingpeng},
journal={arXiv preprint arXiv:2211.11160},
year={2022}
}