Unified task knowledge for spoken language understanding and dialog management

Consider a conversational speech system incorporating spoken language understanding (SLU) and dialog manager (DM) modules. A prerequisite of natural dialog is that understanding takes place in context, which in turn necessitates a sharing of task knowledge between the two modules. However, because they are engaged in different activities it is often the case that the task knowledge representation is different for each of them, an undesirable duplication. In this paper we consider AT&T’s How May I Help You? natural dialog system. We present a method for automatically inducing the task knowledge representation used for spoken language understanding from that used for dialog management. Also, the context information needed by the SLU module for understanding each utterance is generated automatically by the DM from its own knowledge representation and the current dialog situation. This enables understanding-incontext to be implemented while avoiding the duplication involved in creating and maintaining separate task knowledge representations. The system has been evaluated using a database comprising 200k dialogs drawn from live customer traffic.