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Debiaswe: try to make word embeddings less sexist

🔴FAT* 2018 tutorial slides

Here we have the code and data for the following paper: Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings by Tolga Bolukbasi, Kai-Wei Chang, James Zou, Venkatesh Saligrama, and Adam Kalai. Proceedings of NIPS 2016.

Just looking to download a debiased embedding?

You can download binary/txt hard debiased version of the Google's Word2Vec embedding trained on Google News (Origin: GoogleNews-vectors-negative300.bin.gz found here).

Python scripts:

  • given a word embedding and a seed set of gender-specific words (like king, she, etc.), it learns a much larger list of gender-specific words
  • given a word embedding, sets of gender-pairs, gender-specific words, and pairs to equalize, it outputs a new word embedding. This version basically reads/writes word2vec binary file format.
python ../embeddings/GoogleNews-vectors-negative300.bin 50000 ../data/gender_specific_seed.json gender_specific_full.json
python ../embeddings/GoogleNews-vectors-negative300.bin ../data/definitional_pairs.json ../data/gender_specific_full.json ../data/equalize_pairs.json ../embeddings/GoogleNews-vectors-negative300-hard-debiased.bin

We also have seed data used to debias and crowd data used to evaluate the embeddings.

Data files:

  • gender_specific_seed.json: A list of 218 gender-specific words
  • gender_specific_full.json: A list of 1441 gender-specific words
  • definitional_pairs.json: The ten pairs of words we use to define the gender direction
  • equalize_pairs.json: Some crowdsourced F-M pairs of words that represent gender direction

(All external files that I refer within this repo can be found in this folder.)