A Two-Level Interpretation of Modality in Human-Robot Dialogue
Lucia E. Donatelli, Kenneth Lai, James D. Pustejovsky · 2020
We analyze the use and interpretation of modal expressions in a corpus of situated human-robot dialogue and ask how to effectively represent these expressions for automatic learning and dynamic interpretation in context.We present a two-level annotation scheme for modality that captures both content and intent, integrating a logic-based, semantic representation and a task-oriented, pragmatic representation that maps to our robot's capabilities.Data from our annotation task reveals that the interpretation of modal expressions in human-robot dialogue is quite diverse, yet highly constrained by the physical environment and asymmetrical speaker/addressee relationship.We sketch a formal model of human-robot common ground in which modality can be grounded and dynamically interpreted relative to speaker role, temporal constraints, and physical environment.