Enhancing Chatbot Efficiency Through Meta-Analysis and Deep Learning Techniques

EasyChair Preprint 12021

Abstract

Chatbots have become ubiquitous in various domains, serving as virtual assistants, customer service agents, and information providers. Enhancing their performance is crucial to ensure effective user interaction and satisfaction. This paper presents a comprehensive approach to improving chatbot performance by integrating meta-analysis and deep learning techniques. Metaanalysis enables the synthesis of findings from multiple studies, providing insights into the effectiveness of different strategies and methodologies employed in chatbot development. By analyzing a diverse range of studies, we identify common trends, challenges, and opportunities for enhancing chatbot functionality. Additionally, we leverage deep learning approaches to enhance the natural language understanding and generation capabilities of chatbots. Deep learning models such as recurrent neural networks (RNNs) and transformer architectures have shown promising results in various natural language processing tasks, including language translation, sentiment analysis, and dialogue generation. We explore how these models can be adapted and optimized for chatbot applications, considering factors such as data preprocessing, model architecture, and training strategies. By combining meta-analysis insights with advanced deep learning methodologies, we aim to develop more robust and efficient chatbot systems capable of delivering personalized and engaging user experiences.

Keyphrases: Chatbots, Enhancement, Natural Language Processing, Performance, Recurrent, Reinforcement Learning, Transformer Models, deep learning, meta-analysis, neural networks, user interaction

@booklet{EasyChair:12021,
  author    = {Asad Ali},
  title     = {Enhancing Chatbot Efficiency Through Meta-Analysis and Deep Learning Techniques},
  howpublished = {EasyChair Preprint 12021},
  year      = {EasyChair, 2024}}