On Measuring and Mitigating Biased Inferences of Word Embeddings
Table 1: Gender-occupation neutrality scores, for models using GloVe, ELMo, and BERT embeddings.
['Embedding', 'NN', 'FN', 'T:0.5', 'T:0.7']
These numbers are roughly similar across models, and are far from the desired values of 1. This demonstrates gender bias in both static and contextualized embeddings. However, this does not significantly change the neutrality on gender specific inference task. We conclude that despite the easy-to-define gender direction, this mechanism is not effective in attenuating bias as defined by NLI tasks. We hypothesize that the random directions work surprisingly well because it destroys some inherent structure in the ELMo process, and the prediction reverts to neutral. is within 3% change, some negative, some positive. For instance, the probability of predicting neutral is now over 0.5, an increase of +28.4%, and the fraction of examples with neutral probability >0.7 increased from 0.063