Meta-Analysis of Machine Learning Algorithms for Deep Learning Chatbots

EasyChair Preprint no. 11803

Abstract

Machine learning algorithms have gained significant attention in the development of deep learning chatbots. However, the effectiveness and performance of these algorithms across different chatbot applications remain a topic of investigation. In this study, we conducted a meta-analysis of machine learning algorithms used in deep learning chatbots to provide insights into their performance and identify the most effective approaches. Through a comprehensive review of relevant studies, we collected data on the performance metrics and experimental results of various machine learning algorithms in chatbot development. The collected data were analyzed using statistical techniques to evaluate the overall performance and identify trends and patterns across different algorithms. Our findings indicate that several machines learning algorithms, including recurrent neural networks (RNNs), convolutional neural networks (CNNs), and generative adversarial networks (GANs), have shown promising results in improving chatbot performance. RNNs, in particular, have demonstrated strong capabilities in sequence modeling and dialogue generation tasks. Furthermore, the meta-analysis revealed that the performance of machine learning algorithms can vary depending on the specific chatbot application and dataset used. It highlights the importance of considering the contextual factors and application-specific requirements when selecting and fine-tuning machine learning models for chatbot development.

Keyphrases: Algorithms, Chatbot, deep learning, machine learning, Meta_Analysis

@Booklet{EasyChair:11803,
  author = {William Jack and Rookie Joke},
  title = {Meta-Analysis of Machine Learning Algorithms for Deep Learning Chatbots},
  howpublished = {EasyChair Preprint no. 11803},

  year = {EasyChair, 2024}}