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View Code? Open in Web Editor NEWRemove problematic gender bias from word embeddings.
Home Page: https://arxiv.org/abs/1607.06520
License: MIT License
Remove problematic gender bias from word embeddings.
Home Page: https://arxiv.org/abs/1607.06520
License: MIT License
Hi, I was wondering if you could release the code related to soft debiasing? It would be nice to see the comparison between soft and hard debiasing.
Hello Debiaswe Research Team, Thank you for making the code related to your paper available. This is very helpful! I am writing to seek clarification on analyzing gender bias in word vectors associated with professions.
In your paper, you suggest using cosine similarity between a given profession vector and the top PCA component. I am trying to replicate the same in the wiki context. Unfortunately, I am getting results opposite to expected.
For example, when I compute the cosine similarity between the waitress vector (or nurse vector) and the top gender principal component, I get a -ve score. However, when I compute the cosine similarity between the same profession vector and she - he vector (as you show in the example here), I get a +ve score.
I am confused about why the sign flips when using PCA and straightforward gender vector. I request your help.
Thank you!
sbs
Does it has a limitation for the size of the original word2vec bin file? I am trying to use debias.py for a ~12G pre-trained word2vec, but it returns a memoryerror.
Basically, all links to the external files no longer work.
Please provide new links and/or fix the old ones.
I was looking in the code and wondered if it handled the indirect bias. So I wonder if it really exists there and I just can not get it.
Another thing, in the python tutorial notebook, computed the professions with respect to the projection score,as
sp = sorted([(E.v(w).dot(v_gender), w) for w in profession_words])
so I wonder if this is the same like computing the direct bias.
Hi, I was wondering if you're planning on release a set of debiased embeddings which also address racial bias? You mention it in the related paper that it exists but you don't address it here. Will there be a follow-up work on this. It would be good to have pretrained word embeddings which address this as well.
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