A Game Theoretic Approach to Class-wise Selective Rationalization

This repo contains the Tensorflow implementation of A Game Theoretic Approach to Class-wise Selective Rationalization (CAR). To make this repo neat and light-weight, we release the core code with a single multi-aspect dataset (i.e. the beer review) for the demo purpose. If you are interested in reproducing the exact results for other datasets, please contact us, and we are very happy to provide the code and help.

A short video explains the main concepts of our work. For more detail about CAR, Please see our NeurIPS 2019 paper. If you find this work useful and use it in your research, please consider citing our paper.

@article{chang2019rationale,
  title={A Game Theoretic Approach to Class-wise Selective Rationalization},
  author={Chang, Shiyu and Zhang, Yang and Yu, Mo and Jaakkola, Tommi},
  journal={arXiv preprint arXiv:1910.12853},
  year={2019}
}

Getting Started

Below is the step-by-step instruction for running our code. Please first contact the authors for detailed instructions to obtain the access and URL to download the dataset. After cloning the repo, you could replace the URL placeholder in download.sh. Then, you are all set to download the dataset and the pre-trained word embeddings by running:

sh download_data.sh

After the download, you will now see two folders in your main directory, which are data and embeddings. It is worth mentioning that, aspect 0,1, and 2 in the dataset are corresponding to the appearance, aroma, and palate aspect, respectively.

Next, you need to properly set up your virtual environment and install all dependencies from the requirements.txt using the commend:

pip install -r requirements.txt

Once all packages are installed, now you are free to generate some interesting rationales by using the following commends under your main directory.

sh scripts/run_beer_0.sh

You are expected to see a similar result as the following.

The annotation performance: sparsity: 11.9761, precision: 76.2066, recall: 49.2948, f1: 59.8653

Tested environment: Tensorflow: 1.12, CUDA driver: 384.183, and CUDA version: 9.0

Final Words

That's all for now and hope this repo is useful to your research. For any questions, please create an issue and we will get back to you as soon as possible.