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About

Claire Bonial is a computational linguist and dialogue systems researcher whose work sits at the intersection of natural language processing, human-robot interaction, and semantic representation. Her research focuses primarily on enabling more natural, intuitive communication between humans and robots, with particular emphasis on developing robust dialogue systems for real-world applications such as military reconnaissance and search-and-rescue operations. Bonial has made significant contributions to the application of Abstract Meaning Representation (AMR) in human-robot dialogue, proposing novel augmentations to AMR that better capture the nuances of situated, task-oriented communication — work that has helped bridge the gap between linguistic theory and practical robotics deployment. Her pioneering use of the Wizard-of-Oz methodology to collect and analyze human-robot dialogue data has proven especially influential, generating foundational corpora and insights into how people naturally issue commands to robots, including the surprising variation in verbosity and instruction structure that emerges organically among users. With her most-cited paper accumulating 26 citations and a consistent publication record spanning collaborative navigation, dialogue strategy assessment, and automated response classification, Bonial's work has meaningfully shaped the growing field of grounded language understanding in robotics. Her research carries clear real-world stakes, advancing how soldiers and first responders might one day communicate seamlessly with autonomous systems.