We present a dataset for evaluating the grammaticality of the predictions of a language model.We automatically construct a large number of minimally different pairs of English sentences, each consisting of a grammatical and an ungrammatical sentence.The sentence pairs represent different variations of structure-sensitive phenomena: subject-verb agreement, reflexive anaphora and negative polarity items.We expect a language model to assign a higher probability to the grammatical sentence than the ungrammatical one.In an experiment using this data set, an LSTM language model performed poorly on many of the constructions.Multi-task training with a syntactic objective (CCG supertagging) improved the LSTM's accuracy, but a large gap remained between its performance and the accuracy of human participants recruited online.This suggests that there is considerable room for improvement over LSTMs in capturing syntax in a language model.

Mimicry, the automatic imitation of gestures, postures, mannerisms, and other motor movements, has been shown to be a critical component of human interaction but needs further exploration in human-robot interaction. Understanding mimicry is important for building better robots, learning about human categorization of robots in social ingroups/outgroups, and understanding social contagion in human-robot interaction. We investigate the extent to which humans will mimic a robot during the task of describing paintings by comparing the time participants put their hands on their hips before and after observing a robot cue that behavior. We observed no significant difference in participants' hands on hips time before and after the robot's cue. However, we did find that some participants performed the behavior more after the robot's cue while others performed it less. Furthermore, the direction of this change was a function of whether or not a participant performed the specified behavior prior to the robot's cue. This was similarly observed both for frequency of behavior performance and for a second behavior (hands behind back). As such, this study informs future research on human-robot mimicry, particularly on the importance of prior behavior during a human-robot interaction. In doing so, this study provides a baseline for further understanding and exploring mimicry in human-robot interaction as well as evidence for a social component in human-robot mimicry.