Context Is Key: Annotating Situated Dialogue Relations in Multi-floor Dialogue

Claire Bonial, Mitchell Abrams, Anthony L Baker, Taylor Hudson, Stephanie M. Lukin, David Traum, Clare R. Voss

Abstract: In order to account for the features of situated dialogue, we extend a multi-party, multifloor dialogue annotation schema so that it uniquely marks turns with language that must be grounded to the conversational or situational context. We then annotate a dataset of 168 human-robot dialogues using our extended, situated relation schema. Despite the addition of nuanced dialogue relations that reflect the kind of context referenced in the language, our inter-annotator agreement rates remain similar to those of the original annotation schema. Crucially, our updates separate data that can be used to train dialogue systems in essentially any context from those utterances in the data that are only appropriate in a particular situated environment.