  In open-domain dialogue systems, generative approaches have attracted much
attention for response generation. However, existing methods are heavily
plagued by generating safe responses and unnatural responses. To alleviate
these two problems, we propose a novel framework named Dual Adversarial
Learning (DAL) for high-quality response generation. DAL is the first work to
innovatively utilizes the duality between query generation and response
generation to avoid safe responses and increase the diversity of the generated
responses. Additionally, DAL uses adversarial learning to mimic human judges
and guides the system to generate natural responses. Experimental results
demonstrate that DAL effectively improves both diversity and overall quality of
the generated responses. DAL outperforms the state-of-the-art methods regarding
automatic metrics and human evaluations.
