Code for the paper "Generating Hierarchical Explanations on Text Classification via Feature Interaction Detection"

• torchtext == 0.4.0
• gensim == 3.4.0
• pytorch == 1.2.0
• numpy == 1.16.4

Download well-trained models and data.

We provide the example code of HEDGE interpreting the LSTM, CNN and BERT model on the IMDB dataset. We adopt the BERT-base model built by huggingface: https://github.com/huggingface/transformers.

In each folder, run the following command to generate explanations on the test data for a well-trained model.

python hedge_main_model_imdb.py --save /path/to/your/model

We save the start-end word indexes of text spans in a hierarchy (in the order of importance) into the "hedge_interpretation_index.txt" file.

To visualize the hierarchical explanation of a sentence, run

python hedge_main_model_imdb.py --save /path/to/your/model --visualize 1(the index of the sentence)

If you find this repository helpful, please cite our paper:

@inproceedings{chen2020generating,
  title={Generating hierarchical explanations on text classification via feature interaction detection},
  author={Chen, Hanjie and Zheng, Guangtao and Ji, Yangfeng},
  booktitle={ACL},
  url={https://arxiv.org/abs/2004.02015},
  year={2020}
}