  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.
