Performance comparison of representative chatbots based on deep learning

Chuqi Song

doi:10.54254/2755-2721/6/20230936

Research Article

Open access

Published on 14 June 2023

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Song,C. (2023). Performance comparison of representative chatbots based on deep learning. Applied and Computational Engineering,6,710-718.

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Performance comparison of representative chatbots based on deep learning

Chuqi Song *^,1,

¹ 356 Alexandra Road, ALEXIS, 159949, Singapore

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/6/20230936

Abstract

Chatbots have always been a hot topic in the field of natural language processing, which aims to train models to understand input content and give cognitively logical answers. With the continuous development of artificial intelligence technology, the performance of chat robots has continuously made breakthroughs, and has been widely used in many fields such as smart home, e-commerce, and automatic consultation. In this article, we introduce the basic techniques and methods of deep learning-based chatbots, including recurrent neural network (RNN), long short-term memory (LSTM), Seq2Seq model, attention mechanism, etc. We also review a chatbot designed using RNN and quality testing result. We finally summarize the existing research issues of the chatbot and discuss its possible future works.

Keywords

Deep learning, Chatbot, Natural language processing, Recurrent Neural Network, Long Short-Term Memory

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References

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Cite this article

Song,C. (2023). Performance comparison of representative chatbots based on deep learning. Applied and Computational Engineering,6,710-718.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

Conference website: http://www.confspml.org

ISBN：978-1-915371-59-1(Print) / 978-1-915371-60-7(Online)

Conference date: 25 February 2023

Editor：Omer Burak Istanbullu

Series: Applied and Computational Engineering

Volume number: Vol.6

ISSN：2755-2721(Print) / 2755-273X(Online)

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