Efficient Interactive Retrieval of Spo Ranked by Reinforcem

Unlike written documents, spoken documents are difficult to display on the screen; it is also difficult for users to browse these documents during retrieval. It has been proposed recently to use interactive multi-modal dialogues to help the user navigate through a spoken document archive to retrieve the desired documents. This interaction is based on a topic hierarchy constructed by the key terms extracted from the retrieved spoken documents. In this paper, the efficiency of the user interaction in such a system is further improved by a key term ranking algorithm using Reinforcement Learning with simulated users. Significant improvements in retrieval efficiency, which are relatively robust to the speech recognition errors, are observed in preliminary evaluations.