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Conception itérative et semi-supervisée d'assistants conversationnels par regroupement interactif des questions

Abstract : The design of a dataset needed to train a chatbot is most often the result of manual and tedious step. To guarantee the efficiency and objectivity of the annotation, we propose an active learning method based on constraints annotation. It’s an iterative approach, relying on a clustering algorithm to segment data and using annotator knowledge to lead clustering from unlabeled question to relevant intents structure. In this paper, we study the optimal modeling parameters to get an exploitable dataset with a minimum of annotations, and show that this approach allows to make a coherent structure for the training of a chatbot.
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https://hal.inria.fr/hal-03133007
Contributor : Erwan Schild Connect in order to contact the contributor
Submitted on : Friday, February 5, 2021 - 3:36:37 PM
Last modification on : Saturday, October 16, 2021 - 11:26:06 AM
Long-term archiving on: : Friday, May 7, 2021 - 8:28:00 AM

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Schild et al. - 2021 - Concept...
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  • HAL Id : hal-03133007, version 1

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Erwan Schild, Gautier Durantin, Jean-Charles Lamirel, Florian Miconi. Conception itérative et semi-supervisée d'assistants conversationnels par regroupement interactif des questions. EGC 2021 - 21èmes Journées Francophones Extraction et Gestion des Connaissances, Association EGC, Jan 2021, Montpellier / Virtual, France. ⟨hal-03133007⟩

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