Deep learning-based natural language processing in human–agent interaction: Applications, advancements and challenges

Abstract

Human–Agent Interaction is at the forefront of rapid development, with integrating deep learning techniques into natural language processing representing significant potential. This research addresses the complicated dynamics of Human–Agent Interaction and highlights the central role of Deep Learning in shaping the communication between humans and agents. In contrast to a narrow focus on sentiment analysis, this study encompasses various Human–Agent Interaction facets, including dialogue systems, language understanding and contextual communication. This study systematically examines applications, algorithms and models that define the current landscape of deep learning-based natural language processing in Human–Agent Interaction. It also presents common pre-processing techniques, datasets and customized evaluation metrics. Insights into the benefits and challenges of machine learning and Deep Learning algorithms in Human–Agent Interaction are provided, complemented by a comprehensive overview of the current state-of-the-art. The manuscript concludes with a comprehensive discussion of specific Human–Agent Interaction challenges and suggests thoughtful research directions. This study aims to provide a balanced understanding of models, applications, challenges and research directions in deep learning-based natural language processing in Human–Agent Interaction, focusing on recent contributions to the field.