Data-Driven Interaction Patterns: Authority and Information Sharing in Dialogue

We explore the utility of a computational framework for social authority in dialogue, codified as utterance-level annotations. We first use these annotations at a macro level, compiling aggregate statistics and showing that the resulting features are predictive of group performance in a task-based dialogue. Then, at a micro level, we introduce the notion of an interaction pattern, a formulation of speaker interactions over multiple turns. We use these patterns to characterize situations where speakers do not share information equally. These patterns are found to be more discriminative at this task than similar patterns using standard dialogue acts.

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