Volume 12, Number 21, November 2022

Generic Question Classification for Dialogue Systems

  Authors

Marine Troadec, Matthis Houles and Philippe Blache, LPL-CNRS, ILCB, Aix-en-Provence, France

  Abstract

We present in this paper a new classification approach for identifying questions during human-machine interactions and more specifically in dialogue systems. The difficulty in this task is first to be domainindependent, reusable whatever the dialogue application and second to be capable of a real time processing, in order to fit with the needs of reactivity in dialogue systems. The task is then different than that of question classification usually addressed in question-answering systems. We propose in this paper a hierarchical classifier in two steps, filtering first question/no-question utterances and second the type of the question. Our method reaches a f-score of 98% for the first step and 97% for the second one, representing the state of the art for this task.

  Keywords

Question classification, Dialogue systems, Hierarchical classification.