Skip to content
No description, website, or topics provided.
Jupyter Notebook
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
data
source
LICENSE
README.md

README.md

Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them

This project includes the experiments described in the paper:

"Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them", Hila Gonen and Yoav Goldberg, NAACL 2019.

Full reimplementation of the experiments is available in "remaining_bias_2016.ipynb" for Bolukbasi's embeddings, and in "remaining_bias_2018.ipynb" for Zhao's embeddings.

Prerequisites

  • Python 2.7

Download embeddings

As a first step, download the nondebiased and debiased embeddings into data/embeddings/ from this folder (8 files):

  • orig_w2v: Bolukbasi's embeddings, nodebiased
  • hard_debiased_w2v: Bolukbasi's embeddings, debiased
  • orig_glove: Zhao's embeddings, nodebiased
  • gn_glove: Zhao's embeddings, debiased

These files are the original embeddings but with a preprocessing step (for fast loading, see source/save_embeds.py):

Cite

If you find this project useful, please cite the paper:

@inproceedings{GONEN19,
  title={Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them},
  author={Gonen, Hila and Goldberg, Yoav},
  booktitle={Proceedings of NAACL-HLT},
  year={2019}
}

Contact

If you have any questions or suggestions, please contact Hila Gonen.

License

This project is licensed under Apache License - see the LICENSE file for details.

You can’t perform that action at this time.