  Stylistic variation is critical to render the utterances generated by
conversational agents natural and engaging. In this paper, we focus on
sequence-to-sequence models for open-domain dialogue response generation and
propose a new method to evaluate the extent to which such models are able to
generate responses that reflect different personality traits.
