Dialog systems are often designed or trained to output human-like responses.
However, some responses may be impossible for a machine to truthfully say (e.g.
"that movie made me cry"). Highly anthropomorphic responses might make users
uncomfortable or implicitly deceive them into thinking they are interacting
with a human. We collect human ratings on the feasibility of approximately 900
two-turn dialogs sampled from 9 diverse data sources. Ratings are for two
hypothetical machine embodiments: a futuristic humanoid robot and a digital
assistant. We find that for some data-sources commonly used to train dialog
systems, 20-30% of utterances are not viewed as possible for a machine. Rating
is marginally affected by machine embodiment. We explore qualitative and
quantitative reasons for these ratings. Finally, we build classifiers and
explore how modeling configuration might affect output permissibly, and discuss
implications for building less falsely anthropomorphic dialog systems.

通过收集大约 900 个对话样本，我们发现用于训练对话系统的一些常见数据源中，20-30％的话语被认为机器不可能说出来。此外，我们还研究了建模配置如何影响输出许可，并讨论了构建不那么虚假拟人对话系统的影响。