Mitigating Gender Bias for Neural Dialogue Generation with Adversarial Learning

Given messages The elimination of discrimination is an important with the same content for different genders, issue that our society is facing. Learning from dialogue models could produce biased responses, human behaviors, machine learning algorithms which have been measured in terms of their politeness have been proven to inherit the prejudices from and sentiment, as well as the existence of humans (Mehrabi et al., 2019). A variety of AI applications biased words (Liu et al., 2019a). Table 1 shows one have demonstrated common prejudices example from a generative dialogue model trained towards particular groups of people (Rodger and on the Twitter dialogue corpus. When we change Pendharkar, 2004; Howard and Borenstein, 2018; the words in the message from "he" to "she", the responses Rose, 2010; Yao and Huang, 2017; Tolan et al., produced by the dialogue model are quite 2019). It is evident from recent research that different. In particular, the dialogue model generates learning-based dialogue systems also suffer from responses with negative sentiments for females.