论文标题
结识您最喜欢的角色：开放域聊天机器人模仿虚构的角色，只有几句话语
Meet Your Favorite Character: Open-domain Chatbot Mimicking Fictional Characters with only a Few Utterances
论文作者
论文摘要
在本文中，我们将模仿虚构人物视为建立引人入胜的对话模型的有希望的方向。为此，我们提出了一项新的实践任务，其中只有每个虚构角色的几个话语可以产生模仿它们的响应。此外，我们提出了一种名为Pseudo Dialog提示（PDP）的新方法，该方法通过使用包含目标字符的说法的提示来利用大规模语言模型的功能来生成响应。为了更好地反映角色的样式，PDP以对话框的形式构建了提示，其中包括角色的话语作为对话记录。由于在提出的任务中只有字符的话语可用，因此PDP使用检索模型从一组预定义的上下文候选人组中与适当的伪文本相匹配。通过人类和自动评估，我们表明PDP产生的响应比基线方法更好地反映虚构字符的风格。
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.
