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Iterative and Semi-Supervised Design of Chatbots Using Interactive Clustering

Iterative and Semi-Supervised Design of Chatbots Using Interactive Clustering

Erwan Schild, Gautier Durantin, Jean-Charles Lamirel, Florian Miconi
Copyright: © 2022 |Volume: 18 |Issue: 2 |Pages: 19
ISSN: 1548-3924|EISSN: 1548-3932|EISBN13: 9781799893691|DOI: 10.4018/IJDWM.298007
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MLA

Schild, Erwan, et al. "Iterative and Semi-Supervised Design of Chatbots Using Interactive Clustering." IJDWM vol.18, no.2 2022: pp.1-19. http://doi.org/10.4018/IJDWM.298007

APA

Schild, E., Durantin, G., Lamirel, J., & Miconi, F. (2022). Iterative and Semi-Supervised Design of Chatbots Using Interactive Clustering. International Journal of Data Warehousing and Mining (IJDWM), 18(2), 1-19. http://doi.org/10.4018/IJDWM.298007

Chicago

Schild, Erwan, et al. "Iterative and Semi-Supervised Design of Chatbots Using Interactive Clustering," International Journal of Data Warehousing and Mining (IJDWM) 18, no.2: 1-19. http://doi.org/10.4018/IJDWM.298007

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Abstract

Chatbots represent a promising tool to automate the processing of requests in a business context. However, despite major progress in natural language processing technologies, constructing a dataset deemed relevant by business experts is a manual, iterative and error-prone process. To assist these experts during modelling and labelling, the authors propose an active learning methodology coined Interactive Clustering. It relies on interactions between computer-guided segmentation of data in intents, and response-driven human annotations imposing constraints on clusters to improve relevance.This article applies Interactive Clustering on a realistic dataset, and measures the optimal settings required for relevant segmentation in a minimal number of annotations. The usability of the method is discussed in terms of computation time, and the achieved compromise between business relevance and classification performance during training.In this context, Interactive Clustering appears as a suitable methodology combining human and computer initiatives to efficiently develop a useable chatbot.