The Amitiés system: Data-driven techniques for automated dialogue

We present a natural-language customer service application for a telephone banking call center, developed as part of the Amitiés dialogue project (Automated Multilingual Interaction with Information and Services). Our dialogue system, based on empirical data gathered from real call-center conversations, features data-driven techniques that allow for spoken language understanding despite speech recognition errors, as well as mixed system/customer initiative and spontaneous conversation. These techniques include robust named-entity extraction, slot-filling Frame Agents, vector-based task identification and dialogue act classification, a Bayesian database record selection algorithm, and a natural language generator designed with templates created from real agents’ expressions. Preliminary evaluation results indicate efficient dialogues and high user satisfaction, with performance comparable to or better than that of current conversational information systems.