Meta-Analysis of Deep Learning Approaches for Machine Learning Chatbots

EasyChair Preprint no. 11832

Abstract

This paper presents a meta-analysis of deep learning approaches for machine learning chatbots. With the rapid advancement of deep learning techniques, chatbots have gained significant attention as intelligent conversational agents. However, there is a need to evaluate the effectiveness of different deep learning models in chatbot applications. In this study, we conducted a comprehensive meta-analysis of existing research papers to assess the performance of various deep learning approaches in chatbot development. The meta-analysis involved collecting and analyzing data from multiple studies, including performance metrics, model architectures, and datasets used. We compared the performance of different deep learning models based on metrics such as accuracy, response generation quality, and user satisfaction. The results of the meta-analysis provide valuable insights into the strengths and weaknesses of different deep learning approaches for chatbots. Overall, the findings indicate that deep learning models, such as recurrent neural networks and transformers, exhibit promising performance in chatbot applications. However, challenges related to data availability, model complexity, and scalability still need to be addressed. This meta-analysis serves as a guide for researchers and practitioners in selecting and optimizing deep learning approaches for machine learning chatbots, contributing to the advancement of intelligent conversational agents.

Keyphrases: conversational agents, deep learning, machine learning chatbots, meta-analysis, Natural Language Processing, neural networks

@Booklet{EasyChair:11832,
  author = {Asad Ali and Hider Ali},
  title = {Meta-Analysis of Deep Learning Approaches for Machine Learning Chatbots},
  howpublished = {EasyChair Preprint no. 11832},

  year = {EasyChair, 2024}}