yukang-lin / vmask

Code for the paper "Learning Variational Word Masks to Improve the Interpretability of Neural Text Classifiers"

VMASK

Code for the paper "Learning Variational Word Masks to Improve the Interpretability of Neural Text Classifiers"

Requirement:

• python == 3.7.3
• gensim == 3.4.0
• pytorch == 1.2.0
• numpy == 1.16.4

Data:

Download the data with splits for CNN/LSTM-VMASK.

Download the data with splits for BERT-VMASK.

Put the data under the same folder with the code.

Train VMASK Models:

For BERT-VMASK, we adopt the BERT-base model built by huggingface: https://github.com/huggingface/transformers. We first trained BERT-base model, and then loaded its word embeddings for training BERT-VMASK. You can download our pretrained BERT-base models, and put them under the same folder with the code.

In each folder, run the following command to train VMASK-based models.

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

Fine-tune hyperparameters (e.g. learning rate, the number of hidden units) on each dataset.

Reference:

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

@inproceedings{chen2020learning,
  title={Learning Variational Word Masks to Improve the Interpretability of Neural Text Classifiers},
  author={Chen, Hanjie and Ji, Yangfeng},
  booktitle={EMNLP},
  url={https://arxiv.org/abs/2010.00667},
  year={2020}
}