[Paper Review] Mitigating Gender Bias for Neural Dialogue Generation with Adversarial Learning

Haochen Liu, Wentao Wang+4 more|arXiv (Cornell University)|Sep 28, 2020

Topic Modeling30 references4 citations

TL;DR

This paper proposes Debiased-Chat, an adversarial learning framework that mitigates gender bias in neural dialogue generation by disentangling unbiased gender features (e.g., 'handsome man') from biased ones (e.g., stereotypical assumptions), enabling models to generate diverse, fair, and contextually appropriate responses without sacrificing response quality or diversity. Experiments on two real-world datasets show significant bias reduction while maintaining high response engagement and gender-specificity.

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. The implementation of the proposed framework is released.

Motivation & Objective

• To address the growing concern of gender bias in neural dialogue systems trained on human-generated conversations.
• To develop a debiasing method that preserves meaningful gender features in responses while eliminating harmful stereotypes.
• To overcome the limitations of existing debiasing techniques that reduce response diversity by forcing identical outputs across genders.
• To design a framework that enables dialogue models to generate distinct, fair, and contextually relevant responses for male- and female-related inputs.

Proposed method

• Introduce the concept of unbiased gender features (e.g., 'handsome man', 'beautiful woman') and biased gender features (e.g., stereotypical assumptions) in dialogue responses.
• Design a disentanglement model to separate unbiased from biased gender features within gender-related utterances.
• Construct an adversarial training framework where a discriminator is trained to detect biased gender features, and the generator is trained to minimize bias while preserving unbiased features.
• Train the dialogue model using a loss function that combines response generation loss, disentanglement loss, and adversarial loss to balance fairness and response quality.
• Use a disentanglement module to identify and preserve reasonable gender-specific terms in responses, ensuring gender distinctions are maintained without bias.
• Apply the framework to sequence-to-sequence dialogue models in a single-turn setting, focusing on mitigating bias in the 'speaking-about' dimension.

Experimental results

Research questions

• RQ1 Can we reduce gender bias in neural dialogue models without degrading response quality or diversity?
• RQ2 How can we preserve meaningful, unbiased gender features in dialogue responses while eliminating stereotypical or biased associations?
• RQ3 To what extent can adversarial learning and disentanglement improve fairness in dialogue generation compared to existing debiasing methods?
• RQ4 Does the proposed framework maintain response engagement and gender-specificity while reducing bias in real-world dialogue datasets?

Key findings

• The Debiased-Chat framework significantly reduces gender bias in dialogue models on two real-world datasets, as measured by fairness metrics.
• The model generates responses that are both fair and diverse, avoiding the homogenization of outputs across genders seen in baseline debiasing methods.
• Responses generated by Debiased-Chat preserve gender-specific, reasonable terms (e.g., 'handsome man', 'beautiful woman') while eliminating biased expressions.
• The framework outperforms baselines such as CDA and WER, which produce identical, dull responses for different genders, by maintaining contextual and gender-specific distinctions.
• The disentanglement model successfully identifies and retains unbiased gender features, enabling the generator to produce distinct, fair, and contextually appropriate responses.
• The adversarial training component effectively minimizes the discriminator’s ability to detect biased features, confirming the model’s robustness to bias.