Mitigating Gender Bias for Neural Dialogue Generation with Adversarial Learning (2020)

Abstract: Dialogue systems play an increasingly important role in various aspects of our daily life. It is evident from recent research that dialogue systems trained on human conversation data are biased. In particular, they can produce responses that reflect people’s gender prejudice. Many debiasing methods have been developed for various NLP tasks, such as word embedding. However, they are not directly applicable to dialogue systems because they are likely to force dialogue models to generate similar responses for different genders. This greatly degrades the diversity of the generated responses and immensely hurts the performance of the dialogue models. In this paper, we propose a novel adversarial learning framework Debiased-Chat to train dialogue models free from gender bias while keeping their performance. Extensive experiments on two real-world conversation datasets show that our framework significantly reduces gender bias in dialogue models while maintaining the response quality.

## introduction the elimination of discrimination is an important issue that our society is facing. learning from human behaviors, machine learning algorithms have been proven to inherit the prejudices from humans @xcite . a variety of ai applications have demonstrated common prejudices towards particular groups of people @xcite @xcite @xcite . it is evident from recent research that learning-based dialogue systems also suffer from discrimination problems @xcite . dialogue models show significant prejudices towards certain groups of people by producing biased responses to messages related to different genders @xcite . a biased dialogue system will produce improper speeches, which can bring in bad experiences to users or even cause negative social impacts @xcite @xcite . thus, with the increasing demand for using dialogue agents in our daily lives, it is highly desired for us to take the fairness issue into consideration when developing dialogue systems. the gender bias foot_0 in dialogues comes from different dimensions -the gender of the person that speakers are talking about (speaking-about), and the gender of the speaker (speaking-as) and the addressee (speaking-to) @xcite . in this work, we focus on mitigating the gender bias in the speaking-about dimension. it is the most common format of gender bias in dialogues which exists under both speaker-given dialogue scenario, where the personas of the speaker or the addressee are known @xcite , and speaker-agnostic dialogue scenario, where the information of the speakers is unknown. given messages with the same content for different genders, dialogue models could produce biased responses, which have been measured in terms of their politeness and sentiment, as well as the existence of biased words @xcite . ## the proposed framework in this section, we detail the proposed framework. note that in this work, we focus on the classical generative seq2seq dialogue model for single-turn dialogue generation while we leave other settings such as the multi-turn case as future work. we first define two key concepts. we refer to the reasonable and fair gender features in a response as the unbiased gender features of the response. they include gendered terms and words or phrases specially used to describe one gender. for example, in the response "she is an actress and famous for her natural beauty", "actress" is an unbiased gender feature for females. we call the unreasonable and discriminatory gender features in a response as the biased gender features. according to the definition of the bias in dialogue models in @xcite , any offensive, sentimental expressions and biased words correlated with one gender are considered as its biased gender features. for instance, given the same message with different genders as shown in ## experiment in this section, we validate the effectiveness of the proposed framework. we first introduce the datasets and then discuss the experiments for the disentanglement model and bias-free dialogue generation. finally, we further demonstrate the framework via a case study. ## related work the fairness problems in natural language processing have received increasing attention @xcite . word embeddings exhibit human bias for text data. researchers find that in word embeddings trained on large-scale real-world text data, the word "man" is mapped to "programmer" while "woman" is mapped to "homemaker" @xcite . they propose a 2-step method for debiasing word embeddings. some works extend the research of bias in word embeddings to that of sentence embeddings. may et al. (2019 ) propose sentence encoder association test (seat) based on word embedding association test (weat) (islam @xcite . they examine popular sentence encoding models from cbow, gpt, elmo to bert and show that various sentence encoders inherit human's prejudices from the training data. for the task of coreference resolution, a benchmark named winobias is proposed in @xcite to measure the gender bias. this work provides he is a very handsome man. i love her and she is a beautiful woman and she is a beautiful woman. a debiasing method based on data augmentation. @xcite first explore the gender bias in language models. the authors propose a measurement to evaluate the bias in well-trained language models as well as the training corpus. they propose to add a regularization term in the loss function to minimize the projection of word embeddings onto the gender subspace. dialogue systems have been shown to be sensitive to the input messages @xcite @xcite . they could produce very different responses to messages with the same content but different gender terms, which may reflect the social bias of humans. @xcite first study the bias in dialogue systems. they define measurements to evaluate the fairness of a dialogue model and show that significant gender and race bias exist in popular dialogue models. @xcite analyze gender bias in persona-based dialogue models and proposes a combination debiasing method. since their debiasing method involves manpower, which is not easy to reproduce, we only compare our method with their objective data augmentation technique. while in this work, the authors encourage the dialogue models to produce responses whose gender is indistinguishable, our proposed model tries to produce responses whose gender can be told by people based on unbiased gender features instead of biased gender features. ## conclusion in this work, we focus on the problem of mitigating gender bias in neural dialogue models. we propose an adversarial training framework debiased-chat to reduce the bias of a dialogue model during the training process. with the help of a disentanglement model, we design an adversarial learning framework that trains dialogue models to cleverly include unbiased gender features and exclude biased gender features in responses. experiments on two human conversation datasets demonstrate that our model successfully mitigates gender bias in dialogue models and outperforms baselines by producing more engaging, diverse, and gender-specific responses. in the future, we will investigate debiasing retrieval-based dialogue models and more complicated pipeline-based dialogue systems.