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[3J2-GS-6b-04] An Interactive FAQ System with Ability to Understand What Users Want to Know
Keywords:Dialogue system, FAQ, Machine learning
We propose interactive FAQ systems that have ability to explain what users want. In recent years, many services that explain product manuals such as FAQ systems have been provided. However, users may not always be able to express functions of products they want to know in a language. Furthermore, the users may even have misunderstandings about functions. Therefore, we develop an interactive FAQ system with ability to understand what users want even if they ask ambiguous questions and have misunderstandings. Using logistic regression, we trained models that predict car functions and misunderstandings about cars from user utterances. As a result of the evaluation, it was possible to confirm the high accuracy of function predictions and misunderstanding predictions despite the relatively small amount of training data. In addition, the results of the subjective evaluation confirmed that this system was easy for users to use.
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