Abstract
Using dialog systems to automatize customer services is becoming a common practice in many business fields. These dialog systems are often required to relate the users’ issues with a department of the company, which is especially hard when each department covers a wide range of topics. This paper proposes an entropy-based classifier to support the dialog system’s dialog manager in the decision-making. As the classifier is implemented in a feedback-available scenario, an extra class has been inserted with which uncertain cases are labeled, in order to retrieve more information from the user. This way, robust decisions are always ensured. The classifier’s input is the sequence of semantic units decoded from the user turn, extracted from technical records in this paper. This allows the designers to introduce domain specific knowledge and reduce the classifier’s workload. Experiments show that the classifier achieves a high precision, slightly improving some SVM and Bayes classifiers.
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References
Gorin, A.L., Riccardi, G., Wright, J.H.: How may i help you? Speech Commun. 23(1–2), 113–127 (1997)
Chu-Carroll, J., Carpenter, B.: Vector-based natural language call routing. Comput. Linguist. 25(3), 361–388 (1999)
Dinarelli, M., Stepanov, E., Varges, S., Riccardi, G.: The luna spoken dialogue system: Beyond utterance classification. In: 2010 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), pp. 5366–5369 (March 2010)
Li, Q., Tur, G., Hakkani-Tur, D., Li, X., Paek, T., Gunawardana, A., Quirk, C.: Distributed open-domain conversational understanding framework with domain independent extractors. IEEE Institute of Electrical and Electronics Engineers (December 2014)
Inoue, R., Kurosawa, Y., Mera, K., Takezawa, T.: A question-and-answer classification technique for constructing and managing spoken dialog system. In: 2011 International Conference on Speech Database and Assessments (Oriental COCOSDA), pp. 97–101 (October 2011)
Mu, Y., Yin, Y.: Task-oriented spoken dialogue system for humanoid robot. In: 2010 International Conference on Multimedia Technology (ICMT), pp. 1–4. IEEE (October 2010)
Young, S., Gasic, M., Thomson, B., Williams, J.: Pomdp-based statistical spoken dialog systems: A review. Proceedings of the IEEE 101(5), 1160–1179 (2013)
Gupta, N., Tur, G., Hakkani-Tur, D., Bangalore, S., Riccardi, G., Gilbert, M.: The at&t spoken language understanding system. IEEE Transactions on Audio, Speech, and Language Processing 14(1), 213–222 (2006)
Ghigi, F., Torres, M.I.: Decision making strategies for finite state bi-automaton in dialog management. In: 2015 IEEE International Workshop Series on Spoken Dialogue Systems Technology (IWSDS) (January 2015)
Griol, D., Hurtado, L.F., Segarra, E., Sanchis, E.: A statistical approach to spoken dialog systems design and evaluation. Speech Communication 50(89), 666–682 (2008)
Morchid, M., Dufour, R., Bousquet, P.M., Bouallegue, M., Linares, G., De Mori, R.: Improving dialogue classification using a topic space representation and a gaussian classifier based on the decision rule. In: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 126–130 (May 2014)
Dyer, M.: Connectionist natural language processing: A status report. In: Sun, R., Bookman, L. (eds.) Computational Architectures Integrating Neural and Symbolic Processes. The Springer International Series In Engineering and Computer Science, vol. 292, pp. 389–429. Springer, US (1995)
Ward, W.: Extracting information in spontaneous speech. In: The 3rd International Conference on Spoken Language Processing (ICSLP 1994) (September 1994)
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: Machine learning in Python. Journal of Machine Learning Research 12, 2825–2830 (2011)
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Serras, M., Perez, N., Torres, M.I., Del Pozo, A., Justo, R. (2015). Topic Classifier for Customer Service Dialog Systems. In: Král, P., Matoušek, V. (eds) Text, Speech, and Dialogue. TSD 2015. Lecture Notes in Computer Science(), vol 9302. Springer, Cham. https://doi.org/10.1007/978-3-319-24033-6_16
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DOI: https://doi.org/10.1007/978-3-319-24033-6_16
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