  Recent advances in machine learning and deep learning have led to the
widespread use of Conversational AI in many practical applications. However, it
is still very challenging to leverage auxiliary information that can provide
conversational context or personalized tuning to improve the quality of
conversations. For example, there has only been limited research on using an
individuals persona information to improve conversation quality, and even
state-of-the-art conversational AI techniques are unable to effectively
leverage signals from heterogeneous sources of auxiliary data, such as
multi-modal interaction data, demographics, SDOH data, etc. In this paper, we
present a novel Persona-Coded Poly-Encoder method that leverages persona
information in a multi-stream encoding scheme to improve the quality of
response generation for conversations. To show the efficacy of the proposed
method, we evaluate our method on two different persona-based conversational
datasets, and compared against two state-of-the-art methods. Our experimental
results and analysis demonstrate that our method can improve conversation
quality over the baseline method Poly-Encoder by 3.32% and 2.94% in terms of
BLEU score and HR@1, respectively. More significantly, our method offers a path
to better utilization of multi-modal data in conversational tasks. Lastly, our
study outlines several challenges and future research directions for advancing
personalized conversational AI technology.
