Advertisement

Entropy-Driven Dialog for Topic Classification: Detecting and Tackling Uncertainty

  • Manex Serras
  • Naiara Perez
  • María Inés Torres
  • Arantza del Pozo
Chapter
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 427)

Abstract

A frequent difficulty faced by developers of Dialog Systems is the absence of a corpus of conversations to model the dialog statistically. Even when such a corpus is available, neither an agenda nor a statistically-based dialog control logic are options if the domain knowledge is broad. This article presents a module that automatically generates system-turn utterances to guide the user through the dialog. These system-turns are not established beforehand, and vary with each dialog. In particular, the task defined in this paper is the automation of a call-routing service. The proposed module is used when the user has not given enough information to route the call with high confidence. Doing so, and using the generated system-turns, the obtained information is improved through the dialog. The article focuses on the development and operation of this module, which is valid for agenda-based and statistical approaches, being applicable in both types of corpora.

Keywords

Dialog system System turn generation Uncertainty detection Information recovery 

References

  1. 1.
    Bohus, D., Rudnicky, A.: The RavenClaw dialog management framework: architecture and systems. Comput. Speech Lang. XXII I(3), 257–406 (2008)Google Scholar
  2. 2.
    Denecke, M., Waibel, A.: Dialogue strategies guiding users to their communicative goals. Eurospeech 3(November), 1339–1342 (1997)Google Scholar
  3. 3.
    Chu-Carroll, J., Carpenter, B.: Vector-based natural language call routing. Comput. Linguist. 25(3), 361–388 (1999)Google Scholar
  4. 4.
    Griol, D., Torres, F., Hurtado, L.F., Grau, S., Sanchis, E., Segarra, E.: Development and evaluation of the DIHANA project dialog system. In: Proceedings of Interspeech-06 Satellite Workshop Dialogue on Dialogues. Multidisciplinary Evaluation of Advanced Speech-based Interactive Systems. Pittsburgh (2006)Google Scholar
  5. 5.
    Gupta, N., Tür, G., Hakkani-Tür, D., Bangalore, S., Riccardi, G., Gilbert, M.: The AT&T spoken language understanding system. IEEE Trans. Audio Speech Lang. Process. 14(1), 213–222 (2006)Google Scholar
  6. 6.
    Misu, T., Kawahara, T.: Dialogue strategy to clarify users queries for document retrieval system with speech interface. Speech Commun. 48(9), 1137–1150 (2006)CrossRefGoogle Scholar
  7. 7.
    Gosain, A., Bhugra, M.: A comprehensive survey of association rules on quantitative data in data mining. In: Information and Communication Technology (ICT 2013) pp. 1003–1008 (2013), http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6558244
  8. 8.
    Yen, S.J., Chen, A.L.P.: An efficient data mining technique for discovering interesting association rules. In: Proceedings of the Eighth International Workshop on Database and Expert Systems Applications, Sep 1997, pp. 664–669 (1997)Google Scholar
  9. 9.
    Serras, M., Perez, N., Torres, M.I., Del Pozo, A., Justo, R.: Topic classifier for customer service dialog systems. In: Proceedings of the 18th International Conference on Text, Speech and Dialogue. Springer (2015)Google Scholar
  10. 10.
    Carreras, X., Chao, I., Padró, L., Padró, M.: Freeling: An open-source suite of language analyzers. In: Proceedings of the 4th Language Resources and Evaluation Conference (LREC 2004) IV, 239–242 (2004). http://hnk.ffzg.hr/bibl/lrec2004/pdf/271.pdf
  11. 11.
    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. J. Mach. Learn. Res. XII, 2825–2830 (2011)Google Scholar
  12. 12.
    Ghigi, F., Torres, M.I., Justo, R., Benedí, J.M.: Evaluating spoken dialogue models under the interactive pattern recognition framework. In: Proceedings of the INTERSPEECH 2013. pp. 480–484. Lyon (2013)Google Scholar

Copyright information

© Springer Science+Business Media Singapore 2017

Authors and Affiliations

  • Manex Serras
    • 1
  • Naiara Perez
    • 1
  • María Inés Torres
    • 2
  • Arantza del Pozo
    • 1
  1. 1.Vicomtech-IK4Donostia-San SebastianSpain
  2. 2.SPIN Research GroupUniversidad del País Vasco UPV/EHUBilbaoSpain

Personalised recommendations