研究机器人对话中自我拟人化表现，提出可调控的拟人对话新范式。
From Pixels to Personas: Investigating and Modeling Self-Anthropomorphism in Human-Robot Dialogues
- 构建新型对话数据集Pix2Persona，提供拟人与非拟人化响应对。
- 发现拟人化对话在情感表达上显著更丰富，但需权衡伦理风险。
- 适合关注AI人格设计、人机交互伦理的研究者与开发者。
机器人在对话中表现出自我拟人化特征，如表达偏好与情绪。本研究系统分析多个对话数据集中自我拟人化表达的差异，揭示拟人化与非拟人化回应在语义和情感维度上的显著区别，并提出在二者间动态转换的方法。为此，我们构建了Pix2Persona数据集，该数据集保留了现有语料库中的原始对话，并为每个机器人回复添加对应的拟人化与非拟人化配对响应。这项工作不仅识别出此前被忽视的机器人回应新类别，也为未来实现符合伦理规范与用户期望的可调节拟人度智能系统奠定了基础。
原文摘要 · Abstract (English)
Self-anthropomorphism in robots manifests itself through their display of human-like characteristics in dialogue, such as expressing preferences and emotions. Our study systematically analyzes self-anthropomorphic expression within various dialogue datasets, outlining the contrasts between self-anthropomorphic and non-self-anthropomorphic responses in dialogue systems. We show significant differences in these two types of responses and propose transitioning from one type to the other. We also introduce Pix2Persona, a novel dataset aimed at developing ethical and engaging AI systems in various embodiments. This dataset preserves the original dialogues from existing corpora and enhances them with paired responses: self-anthropomorphic and non-self-anthropomorphic for each original bot response. Our work not only uncovers a new category of bot responses that were previously under-explored but also lays the groundwork for future studies about dynamically adjusting self-anthropomorphism levels in AI systems to align with ethical standards and user expectations.
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