Revisiting Biased Word Embeddings
Linear Digressions
English - June 24, 2019 00:26 - 18 minutes - 8.31 MB - ★★★★★ - 350 ratingsTechnology data science machine learning linear digressions Homepage Download Apple Podcasts Google Podcasts Overcast Castro Pocket Casts RSS feed
The topic of bias in word embeddings gets yet another pass this week. It all started a few years ago, when an analogy task performed on Word2Vec embeddings showed some indications of gender bias around professions (as well as other forms of social bias getting reproduced in the algorithm’s embeddings). We covered the topic again a while later, covering methods for de-biasing embeddings to counteract this effect. And now we’re back, with a second pass on the original Word2Vec analogy task, but where the researchers deconstructed the “rules” of the analogies themselves and came to an interesting discovery: the bias seems to be, at least in part, an artifact of the analogy construction method. Intrigued? So were we…
Relevant link:
https://arxiv.org/abs/1905.09866