Evaluating Chatbot Performance: A Meta-Analysis Approach with Deep Learning

Kyldo Jsowd

No 593tq, OSF Preprints from Center for Open Science

Abstract: Chatbot technology has gained significant attention in recent years, with numerous studies focusing on developing and evaluating chatbot performance. However, due to the vast amount of research and the diversity of methodologies employed, it can be challenging to gain a comprehensive understanding of chatbot performance across different domains and applications. In this paper, we propose a meta-analysis approach to evaluate chatbot performance using deep learning techniques. The objective of this study is to systematically analyze and synthesize the findings from existing chatbot performance evaluations, providing a comprehensive assessment of chatbot capabilities and identifying factors that contribute to their success or limitations. To achieve this, we leverage deep learning models to extract valuable insights from a wide range of chatbot evaluation studies.

Date: 2023-07-16
New Economics Papers: this item is included in nep-big and nep-cmp
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Persistent link: https://EconPapers.repec.org/RePEc:osf:osfxxx:593tq

DOI: 10.31219/osf.io/593tq

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