Generic Question Classification for Dialogue Systems

Marine Troadec (1) , Matthis Houlès (1) , Philippe Blache (1)

1 LPL - Laboratoire Parole et Langage

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.

Mots clés: Question classification; Dialogue systems; Hierarchical classification

Domaines: Intelligence artificielle [cs.AI]; Interface homme-machine [cs.HC]; Traitement du texte et du document; Linguistique; Sciences cognitives

Marine Troadec, Matthis Houlès, Philippe Blache. Generic Question Classification for Dialogue Systems. International Conference on NLP & Artificial Intelligence Techniques (NLAI 2022), 2022, London, United Kingdom. ⟨hal-03738902⟩