Kimberly Pollard


2018

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Dialogue Structure Annotation for Multi-Floor Interaction
David Traum | Cassidy Henry | Stephanie Lukin | Ron Artstein | Felix Gervits | Kimberly Pollard | Claire Bonial | Su Lei | Clare Voss | Matthew Marge | Cory Hayes | Susan Hill
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Consequences and Factors of Stylistic Differences in Human-Robot Dialogue
Stephanie Lukin | Kimberly Pollard | Claire Bonial | Matthew Marge | Cassidy Henry | Ron Artstein | David Traum | Clare Voss
Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue

This paper identifies stylistic differences in instruction-giving observed in a corpus of human-robot dialogue. Differences in verbosity and structure (i.e., single-intent vs. multi-intent instructions) arose naturally without restrictions or prior guidance on how users should speak with the robot. Different styles were found to produce different rates of miscommunication, and correlations were found between style differences and individual user variation, trust, and interaction experience with the robot. Understanding potential consequences and factors that influence style can inform design of dialogue systems that are robust to natural variation from human users.

2017

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Exploring Variation of Natural Human Commands to a Robot in a Collaborative Navigation Task
Matthew Marge | Claire Bonial | Ashley Foots | Cory Hayes | Cassidy Henry | Kimberly Pollard | Ron Artstein | Clare Voss | David Traum
Proceedings of the First Workshop on Language Grounding for Robotics

Robot-directed communication is variable, and may change based on human perception of robot capabilities. To collect training data for a dialogue system and to investigate possible communication changes over time, we developed a Wizard-of-Oz study that (a) simulates a robot’s limited understanding, and (b) collects dialogues where human participants build a progressively better mental model of the robot’s understanding. With ten participants, we collected ten hours of human-robot dialogue. We analyzed the structure of instructions that participants gave to a remote robot before it responded. Our findings show a general initial preference for including metric information (e.g., move forward 3 feet) over landmarks (e.g., move to the desk) in motion commands, but this decreased over time, suggesting changes in perception.