  In this paper, we consider mimicking fictional characters as a promising
direction for building engaging conversation models. To this end, we present a
new practical task where only a few utterances of each fictional character are
available to generate responses mimicking them. Furthermore, we propose a new
method named Pseudo Dialog Prompting (PDP) that generates responses by
leveraging the power of large-scale language models with prompts containing the
target character's utterances. To better reflect the style of the character,
PDP builds the prompts in the form of dialog that includes the character's
utterances as dialog history. Since only utterances of the characters are
available in the proposed task, PDP matches each utterance with an appropriate
pseudo-context from a predefined set of context candidates using a retrieval
model. Through human and automatic evaluation, we show that PDP generates
responses that better reflect the style of fictional characters than baseline
methods.
