For conversational AI and virtual assistants to communicate with humans in a realistic way, they must exhibit human characteristics such as expression of emotion and personality. Current attempts toward constructing human-like dialogue agents have presented significant difficulties. We propose Human Level Attributes (HLAs) based on tropes as the basis of a method for learning dialogue agents that can imitate the personalities of fictional characters. Tropes are characteristics of fictional personalities that are observed recurrently and determined by viewers' impressions. By combining detailed HLA data with dialogue data for specific characters, we present a dataset, HLA-Chat, that models character profiles and gives dialogue agents the ability to learn characters' language styles through their HLAs. We then introduce a three-component system, ALOHA (which stands for Artificial Learning of Human Attributes), that combines character space mapping, character community detection, and language style retrieval to build a character (or personality) specific language model. Our preliminary experiments demonstrate that two variations of ALOHA, combined with our proposed dataset, can outperform baseline models at identifying the correct dialogue responses of chosen target characters, and are stable regardless of the character's identity, the genre of the show, and the context of the dialogue.
翻译:对于对话的AI和虚拟助手以现实的方式与人类交流来说,他们必须展示人类特征,例如情感和个性表达。目前试图建设人式对话代理器的努力遇到了巨大的困难。我们提出以大字节为基础的人级属性(HLAs)作为学习对话代理器的基础,可以模仿虚构人物的个性。Trope是虚构人物的特征的特征,经常被观察,并且由观众的印象来决定。通过将详细的HLA数据与特定字符的对话数据相结合,我们提出了一个数据集,HLA-Chat,模型性格剖面图和使对话代理器能够通过他们的HLAs学习字符的风格。我们随后提出一个三个组成部分的系统,ALOHA(代表人类属性的人工学习),将字符空间映射、字符社区探测和语言样式检索结合起来,以建立个性(或个性)特定语言模型。我们的初步实验表明,ALOHA的两种变异,加上我们提议的数据集,在确定选定字符的正确对话反应时,超越了基准模型,显示目标字符的特征的准确背景和稳定的特性。