Abstract
Chatbots and conversational systems are becoming a prominent research area, and many businesses are starting to leverage on their capability to handle basic communication tasks. With a vast variety of available frameworks for chat-bot development from tech giants, business organizations can build their own systems quickly and conveniently. However, these frameworks often lack a proper set of holistic tools to build a chatbot that is manageable, adaptable to learn, and scalable. Hence, frequently, additional machine learning mechanisms are needed to improve performance. In this paper, we demonstrate a chatbot system that uses machine learning to answer Frequently Asked Questions (FAQs) from our school website. The system includes different types of user query and a vector similarity analysis component to handle long and complex user queries. In addition, the Google’ s DialogFlow framework is used for intention detection.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
- 3.
- 4.
- 5.
- 6.
- 7.
- 8.
References
Abu Shawar B, Atwell E (2007) Different measurements metrics to evaluate achatbot system. In: Proceedings of the workshop on bridging the gap: Academic and industrial research in dialog technologies, pp. 89–96. Association for Computational Linguistics
Braun D, Hernandez-Mendez A, Matthes F, Langen M (2017) Evaluating natural language understanding services for conversational question answering systems. In: Proceedings of the 18th Annual SIG dial Meeting on Discourse and Dialogue
Kuligowska K (2015) Commercial chatbot: performance evaluation, usability metrics and quality standards of embodied conversational agents
Quarteroni S, Manandhar S (2007) SA chatbot-based interactive question answering system. Decalog 2007: 83
Yan Z, Duan N, Bao J, Chen P, Zhou M, Li Z (2016) JiansheZhou: Docchat: an information retrieval approach for chatbot engines using unstructured documents. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), vol 1, pp 516–525
Acknowledgements
This research was supported by the Speech team in Multimedia and Interactive Computing Lab (MICL), School of Computer Science and Engineering, NTU, Singapore.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Vu, T.L., Tun, K.Z., Eng-Siong, C., Banchs, R.E. (2021). Online FAQ Chatbot for Customer Support. In: Marchi, E., Siniscalchi, S.M., Cumani, S., Salerno, V.M., Li, H. (eds) Increasing Naturalness and Flexibility in Spoken Dialogue Interaction. Lecture Notes in Electrical Engineering, vol 714. Springer, Singapore. https://doi.org/10.1007/978-981-15-9323-9_21
Download citation
DOI: https://doi.org/10.1007/978-981-15-9323-9_21
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-9322-2
Online ISBN: 978-981-15-9323-9
eBook Packages: EngineeringEngineering (R0)