We present computational models that allow spoken dialog systems to handle multiparticipant engagement in open, dynamic environments, where multiple people may enter and leave conversations, and interact with the system and with others in a natural manner. The models for managing the engagement process include components for (1) sensing the engagement state, actions and intentions of multiple agents in the scene, (2) making engagement decisions (i.e. whom to engage with, and when) and (3) rendering these decisions in a set of coordinated low-level behaviors in an embodied conversational agent. We review results from a study of interactions "in the wild" with a system that implements such a model.
We study how synchronized gaze, gesture and speech rendered by an embodied conversational agent can influence the flow of conversations in multiparty settings. We review a computational framework for turn taking that provides the foundation for tracking and communicating intentions to hold, release, or take control of the conversational floor. We then present details of the implementation of the approach in an embodied conversational agent and describe experiments with the system in a shared task setting. Finally, we discuss results showing how the verbal and non-verbal cues used by the avatar can shape the dynamics of multiparty conversation.
We study the opportunity for using crowdsourcing methods to acquire language corpora for use in natural language processing systems. Specifically, we empirically investigate three methods for eliciting natural language sentences that correspond to a given semantic form. The methods convey frame semantics to crowd workers by means of sentences, scenarios, and list-based descriptions. We discuss various performance measures of the crowdsourcing process, and analyze the semantic correctness, naturalness, and biases of the collected language. We highlight research challenges and directions in applying these methods to acquire corpora for natural language processing applications.
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