Debiaswe: try to make word embeddings less sexist
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?
- learn_gender_specific.py: 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
- debias.py: 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 learn_gender_specific.py ../embeddings/GoogleNews-vectors-negative300.bin 50000 ../data/gender_specific_seed.json gender_specific_full.json
python debias.py ../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.
- 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.)