Intent classification for a management conversational assistant

Hefny, Abdelrahman, Dafoulas, George ORCID: https://orcid.org/0000-0003-2638-8771 and Ismail, Manal (2020) Intent classification for a management conversational assistant. 15th International Conference on Computer Engineering and Systems Proceedings. In: ICCES 2020, 15-16 Dec 2020, Cairo, Egypt. e-ISBN 9780738105598, pbk-ISBN 9780738105604. [Conference or Workshop Item] (doi:10.1109/ICCES51560.2020.9334685)

Abstract

Intent classification is an essential step in processing user input to a conversational assistant. This work investigates techniques of intent classification of chat messages used for communication among software development teams with the aim of building an intent classifier for a management conversational assistant integrated into modern communication platforms used by developers. Experiments conducted using rule-based and common ML techniques have shown that careful choice of classification features has a significant impact on performance, and the best performing model was able to obtain a classification accuracy of 72%. A set of techniques for extracting useful features for text classification in the software engineering domain was also implemented and tested.

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