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.

翻译：在本文中, 我们把模仿虚构字符视为建设有吸引力的对话模式的有希望的方向。 为此, 我们提出了一个新的实用任务, 每一个虚构字符只有几句话可用于生成模拟反应。 此外, 我们提出一个名为 Pseudo Dialog 的新方法, 通过利用大型语言模型的力量来生成响应, 包括目标字符的提示。 为了更好地反映字符的风格, PDP 以对话框的形式构建提示, 包括字符的表达作为对话框的历史。 由于在拟议任务中只有字符的表达, PDP 使用一个检索模型, 将每一个预定义的背景候选人的适当的伪文本匹配起来。 我们通过人文和自动评估显示, PDP 生成的响应比基线方法更能反映虚构字符的风格。