  Building a persona-based conversation agent is challenging owing to the lack
of large amounts of speaker-specific conversation data for model training. This
paper addresses the problem by proposing a multi-task learning approach to
training neural conversation models that leverages both conversation data
across speakers and other types of data pertaining to the speaker and speaker
roles to be modeled. Experiments show that our approach leads to significant
improvements over baseline model quality, generating responses that capture
more precisely speakers' traits and speaking styles. The model offers the
benefits of being algorithmically simple and easy to implement, and not relying
on large quantities of data representing specific individual speakers.
