Identifying and Reducing Gender Bias in Word-Level Language Models
Table 4: Generated text comparison for CNN/Daily Mail for different λ values
['[BOLD] Target Word', '[ITALIC] λ', '[BOLD] Sample From Generated Text']
We highlight the words crying and fragile that are typically associated with feminine qualities, along with the words leadership and prisoners that are stereotyped with male identity. These biases are reflected in the generated text for λ=0. We notice increased mention of the less probable gender in the subsequent generated text with debiasing (λ=0.5,1.0). For fragile, the generated text at λ=1.0 has reduced the mention of stereotyped female words but had no mentions of male words; resulting in a large chunk of neutral text. Similarly, in prisoners, the generated text for λ=0.5 has no gender words.