Title: Measuring chatbot quality of service to predict human-machine hand-over using a character deep learning model

Authors: Ebtesam Hussain Almansor; Farookh Khadeer Hussain; Omar Khadeer Hussain

Addresses: School of Computer Science, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, Australia; Community College, Najran University, Najran, Saudi Arabia ' School of Computer Science, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, Australia ' School of Business, University of New South Wales (UNSW), Canberra, Australia

Abstract: Recently, intelligent dialogue systems have shown promise in terms of reducing the load of human customer care agents and decreasing user wait times. In some cases, these systems still cannot understand user intent which leads to the generation of inappropriate responses. Therefore, their inability to handle inappropriate responses has limited their utility in the real world. In this work, we propose a character deep learning model for the detection of chatbot quality of services to handle inappropriate responses by intelligently transferring the dialogue to a human agent. The proposed model has two goals: detect CQoS based on the sentiment score of the utterance using a deep learning model and transferring the user to a live agent when the utterance is inappropriate. The proposed model's effectiveness is evaluated on the dialogue breakdown detection task. The results of the experiment show that our proposed model is effective in achieving the desired goals.

Keywords: character deep learning model; CQoS; breakdown in dialogue; hand-over mechanism.

DOI: 10.1504/IJWGS.2022.126126

International Journal of Web and Grid Services, 2022 Vol.18 No.4, pp.479 - 495

Received: 01 Sep 2021
Received in revised form: 22 Apr 2022
Accepted: 30 May 2022
Published online: 11 Oct 2022 *

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