Advancing Chatbot Technology: Deep Learning and Meta-Analysis Integration

EasyChair Preprint 11780

Abstract

The rapid advancement of chatbot technology has revolutionized various domains, including customer service, healthcare, and education. However, there is still a need for further improvement in chatbot performance and intelligence. This paper explores the integration of deep learning and meta-analysis techniques to advance chatbot technology. Deep learning, a subset of machine learning, has shown great promise in enhancing chatbot capabilities, such as natural language understanding, dialogue management, and response generation. By leveraging deep neural networks, chatbots can better comprehend and generate human-like responses, improving user satisfaction and engagement. Meta-analysis, on the other hand, provides a systematic approach to synthesizing research findings from multiple studies. By analyzing and combining results from various sources, meta-analysis enables a comprehensive evaluation of chatbot performance, identification of trends, and insights into effective strategies. The integration of deep learning and meta-analysis offers several advantages. Deep learning techniques enable more accurate and efficient analysis of large datasets, while meta-analysis provides a comprehensive and unbiased evaluation of chatbot performance across different studies. By combining these approaches, researchers can gain a deeper understanding of chatbot strengths and weaknesses and make informed decisions for further enhancements. This paper discusses the significance of integrating deep learning and meta-analysis in advancing chatbot technology.

Keyphrases: Chabot, Meta-Analysis Integration, Technology, deep learning

@booklet{EasyChair:11780,
  author    = {Asad Ali and Tahir Abbas and Danish Ali},
  title     = {Advancing Chatbot Technology: Deep Learning and Meta-Analysis Integration},
  howpublished = {EasyChair Preprint 11780},
  year      = {EasyChair, 2024}}