We demonstrate DialBB, a dialogue system development framework, which we have been building as an educational material for dialogue system technology. Building a dialogue system requires the adoption of an appropriate architecture depending on the application and the integration of various technologies. However, this is not easy for those who have just started learning dialogue system technology. Therefore, there is a demand for educational materials that integrate various technologies to build dialogue systems, because traditional dialogue system development frameworks were not designed for educational purposes. DialBB enables the development of dialogue systems by combining modules called building blocks. After understanding sample applications, learners can easily build simple systems using built-in blocks and can build advanced systems using their own developed blocks.

For the acquisition of knowledge through dialogues, it is crucial for systems to ask questions that do not diminish the user’s willingness to talk, i.e., that do not degrade the user’s impression. This paper reports the results of our analysis on how user impression changes depending on the types of questions to acquire lexical knowledge, that is, explicit and implicit questions, and the correctness of the content of the questions. We also analyzed how sequences of the same type of questions affect user impression. User impression scores were collected from 104 participants recruited via crowdsourcing and then regression analysis was conducted. The results demonstrate that implicit questions give a good impression when their content is correct, but a bad impression otherwise. We also found that consecutive explicit questions are more annoying than implicit ones when the content of the questions is correct. Our findings reveal helpful insights for creating a strategy to avoid user impression deterioration during knowledge acquisition.

We address the problem of acquiring the ontological categories of unknown terms through implicit confirmation in dialogues. We develop an approach that makes implicit confirmation requests with an unknown term’s predicted category. Our approach does not degrade user experience with repetitive explicit confirmations, but the system has difficulty determining if information in the confirmation request can be correctly acquired. To overcome this challenge, we propose a method for determining whether or not the predicted category is correct, which is included in an implicit confirmation request. Our method exploits multiple user responses to implicit confirmation requests containing the same ontological category. Experimental results revealed that the proposed method exhibited a higher precision rate for determining the correctly predicted categories than when only single user responses were considered.