Conversational AI has become an increasingly prominent and practical
application of machine learning. However, existing conversational AI techniques
still suffer from various limitations. One such limitation is a lack of
well-developed methods for incorporating auxiliary information that could help
a model understand conversational context better. In this paper, we explore how
persona-based information could help improve the quality of response generation
in conversations. First, we provide a literature review focusing on the current
state-of-the-art methods that utilize persona information. We evaluate two
strong baseline methods, the Ranking Profile Memory Network and the
Poly-Encoder, on the NeurIPS ConvAI2 benchmark dataset. Our analysis elucidates
the importance of incorporating persona information into conversational
systems. Additionally, our study highlights several limitations with current
state-of-the-art methods and outlines challenges and future research directions
for advancing personalized conversational AI technology.

研究旨在探索如何在会话系统中加入角色信息以提高响应生成的质量，并发现当前存在不足和挑战和未来的研究方向。