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Mitigating Gender Bias for Neural Dialogue Generation with Adversarial Learning

机译:对抗对抗性学习的神经对话生成的性别偏见

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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.
机译:对话系统在日常生活的各个方面发挥着越来越重要的作用。最近的研究是显而易见的,对话系统培训的人类会话数据有偏见。特别是,他们可以产生反映人们性别偏见的反应。已经为各种NLP任务开发了许多脱叠方法,例如Word Embedding。但是,它们不可直接适用于对话系统,因为它们可能会强制对话模型来为不同的性别产生类似的响应。这极大地降低了所生成的响应的多样性,并非常伤害对话模型的性能。在本文中,我们提出了一种新的对抗性学习框架脱叠 - 聊天,以培训对话模型,不会在保持性别偏见的同时。对两个真实对话数据集的广泛实验表明,我们的框架在保持响应质量的同时,我们的框架在对话模型中显着降低了性别偏差。释放拟议框架的实施。

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