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

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


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.


Dialog system System turn generation Uncertainty detection Information recovery 


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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

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