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Iterative and Semi-Supervised Design of Chatbots Using Interactive Clustering

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.
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Contributor : Erwan Schild Connect in order to contact the contributor
Submitted on : Thursday, April 21, 2022 - 10:34:32 AM
Last modification on : Saturday, April 23, 2022 - 3:28:52 AM


Schild et al. - 2022 - Iterati...
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Erwan Schild, Gautier Durantin, Jean-Charles Lamirel, Florian Miconi. Iterative and Semi-Supervised Design of Chatbots Using Interactive Clustering. International Journal of Data Warehousing and Mining (IJDWM), IGI Global, 2022, 18 (2), pp.1-19. ⟨10.4018/IJDWM.298007⟩. ⟨hal-03648041⟩



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