Abstract
We have been developing a method for a dialogue system to acquire information (e.g., cuisines of unknown restaurants) through dialogue by asking questions of the users. It is important that the questions are concise and concrete to prevent users from being annoyed. Our method selects the most appropriate question on the basis of expected utility calculated for four types of question: Yes/No, alternative, 3-choice, and Wh- questions. We define utility values for the four types and also derive the probability representing how likely each question is to contain a correct cuisine. The expected utility is then calculated as the sum totals of their products. We empirically compare several ways to integrate two previously proposed basic confidence measures (CMs) when deriving the probability for each question. We also examine the appropriateness of the utility values through questionnaires administered to 15 participants.
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Notes
- 1.
The prior information \(r_k(c)\) and \(Acc_k(c)\) should have been obtained by a held-out data. The information here was obtained in a closed manner, i.e., using the whole 1,656 entries.
References
Bollacker, K., Evans, C., Paritosh, P., Sturge, T., Taylor, J.: Freebase: a collaboratively created graph database for structuring human knowledge. In: Proceedings of the ACM SIGMOD international conference on Management of data, pp. 1247–1250 (2008)
Hakkani-Tur, D., Celikyilmaz, A., Heck, L., Tur, G., Zweig, G.: Probabilistic enrichment of knowledge graph entities for relation detection in conversational understanding. In: Proceedings of Annual Conference of the International Speech Communication Association (INTERSPEECH), pp. 2113–2117 (2014)
Otsuka, T., Komatani, K., Sato, S., Nakano, M.: Generating more specific questions for acquiring attributes of unknown concepts from users. In: Proceedings of Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL), pp. 70–77. Metz, France (2013)
von Neumann, J., Morgenstern, O.: Theory of Games and Economic Behavior. Princeton University Press (1944)
Pappu, A., Rudnicky, A.I.: Learning situated knowledge bases through dialog. In: Proceedings of Annual Conference of the International Speech Communication Association (INTERSPEECH), pp. 120–124 (2014)
Takahashi, Y., Dohsaka, K., Aikawa, K.: An efficient dialogue control method using decision tree-based estimation of out-of-vocabulary word attributes. In: Proceedings of International Conference on Spoken Language Processing (ICSLP), pp. 813–816 (2002)
Meng, H., Ching, P.C., Chan, S.F., Wong, Y.F., Chan, C.C.: ISIS: An adaptive, trilingual conversational system with interleaving interaction and delegation dialogs. ACM Trans. Comput. Hum. Interact. 11(3), 268–299 (2004)
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This study was partly supported by SCOPE of MIC.
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Komatani, K., Otsuka, T., Sato, S., Nakano, M. (2017). Question Selection Based on Expected Utility to Acquire Information Through Dialogue. In: Jokinen, K., Wilcock, G. (eds) Dialogues with Social Robots. Lecture Notes in Electrical Engineering, vol 427. Springer, Singapore. https://doi.org/10.1007/978-981-10-2585-3_6
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DOI: https://doi.org/10.1007/978-981-10-2585-3_6
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