python==3.8.10
transformers==4.1.1
pytorch==1.10.0
Run the code to train the module of RGCN and extract the common sense feature of the data:
python preprocess_graph.py
python train_and_extract_graph_features.py
python extract_graph_features.py
You can also download the trained features and data from https://drive.google.com/file/d/1HYiIVxTTHuqpWhGxqW8q6iFQQSXGNR05/view?usp=sharing.
The original dataset can be downloaded from https://github.com/emilyallaway/zero-shot-stance.
The ConceptGraph can be downloaded from https://drive.google.com/file/d/19klcp69OYEf29A_JrBphgkMVPQ9rXe1k/view.
The code for training of SentiBERT can be found in https://github.com/12190143/SentiX.
python run_bert.py
Some of our code comes from https://github.com/declare-lab/kingdom.
@article{luo2022exploiting,
title={Exploiting Sentiment and Common Sense for Zero-shot Stance Detection},
author={Luo, Yun and Liu, Zihan and Shi, Yuefeng and Zhang, Yue},
journal={arXiv preprint arXiv:2208.08797},
year={2022}
}