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README.md

DiffMask

Overview

This library contains a Pytorch implementation of Differentiable Masking Explainer (Diffmask), as presented in [1](https://arxiv.org/abs/2004.14992).

Dependencies

Notice that older or newer version could work but they were not tested.

Installation

To install, run

$ python setup.py install

To donwload datasets run

$ ./scripts/download_datasets.sh

To download models, use the following link. Note that these are not the exaclt same models used for the paper.

Structure

  • diffmask: Contains the source code for DiffMask.

We have 5 jupyter notebbok with the code for reproducing some of the results from our work [1]. Note that since i) the code was refactored and ii) we were not able to realise the exact same models used for the paper, re-generated plots and tables might differ from the ones in our work.

Please cite [1] in your work when using this library in your experiments.

Feedback

For questions and comments, feel free to contact Nicola De Cao.

License

MIT

Citation

[1] De Cao, N., Schlichtkrull, M., Aziz, W., Titov, I. (2020).
How do Decisions Emerge across Layers in Neural Models? 
Interpretation with Differentiable Masking
In Proceedings of the 2020 Conference on Empirical
Methods in Natural Language Processing.

BibTeX format:

@article{decao2020decisions,
  title={How do Decisions Emerge across Layers in Neural Models?},
  author={
    De Cao, Nicola and
    Aziz, Wilker and
    Titov, Ivan},
  journal={Proceedings of the 2020 Conference on Empirical
           Methods in Natural Language Processing},
  year={2020}
}
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