A question-and-answer classification technique for constructing and managing spoken dialog system

To recognize user speech accurately and respond to it appropriately, a spoken dialog system usually uses a question-and-answer database (QADB) which contains many question-and-answer pairs. The systems first select a question example which is the most similar to the recognition result for the input voice from the database. An answer sentence which is then paired with the selected question example is output to the user. Many systems have a large database to enable a more appropriate answer to be output. However, when such a database is used, the waiting time increases because the system needs to find the most appropriate question example from a vast number of question examples. We propose a method of classifying the queries in the QADB. By classifying question examples into some clusters using pLSA, an appropriate question example can be found more quickly than when using the conventional method. We evaluated the validity of our proposed method by changing various parameters.