On Measuring and Mitigating Biased Inferences of Word Embeddings
Table 11: Effect of attenuating gender bias on all layers of ELMo and with random vectors with difference (diff) from no attenuation.
['Gender (ELMo All Layers)', 'Gender (ELMo All Layers) NN', 'Gender (ELMo All Layers) FN', 'Gender (ELMo All Layers) T:0.5', 'Gender (ELMo All Layers) T:0.7']
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.