  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.
