On Measuring and Mitigating Biased Inferences of Word Embeddings
Table 13: Effect of attenuating gender bias on layer 1 of ELMo with he-she vectors and random vectors with difference (diff) from no attenuation.
['Gender (ELMo Layer 1)', 'Gender (ELMo Layer 1) NN', 'Gender (ELMo Layer 1) FN', 'Gender (ELMo Layer 1) T:0.5', 'Gender (ELMo Layer 1) T:0.7']
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