Interactive Natural Language Processing

Zekun Wang

ISBN 10: 3032062632 / ISBN 13: 9783032062635

Published by Springer-Verlag Gmbh Apr 2026, 2026

About this Item

This item is printed on demand - Print on Demand Titel. Neuware -This book addresses the emergence of Interactive Natural Language Processing (iNLP) and its novel paradigm within the field of NLP and discusses the limitations in existing frameworks while aligning with the ultimate goals of artificial intelligence. This paradigm considers language models as agents capable of observing, acting, and receiving feedback iteratively from external entities. The book provides a comprehensive survey of iNLP, starting by proposing a unified definition and framework of the concept. In addition, it provides a systematic classification of iNLP and dissects its various components, including interactive objects, interaction interfaces, and interaction methods. The book then proceeds to delve into the evaluation methodologies used in the field and explores its diverse applications, scrutinizes its ethical and safety issues, and discusses prospective research directions. This work aims to be an entry point for researchers who are interested in this rapidly evolving area and offers a broad view of the current landscape and future trajectory of iNLP.Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 227 pp. Englisch.

Seller Inventory # 9783032062635

Bibliographic Details

• Title: Interactive Natural Language Processing
• Publisher: Springer-Verlag Gmbh Apr 2026
• Publication Date: 2026
• Language: English
• ISBN 10: 3032062632
• ISBN 13: 9783032062635
• Binding: Buch
• Condition: Neu
• Dimensions: 246W x 173H x 20D millimeters 246 millimeters width by 173 millimeters height by 20 millimeters depth
• Weight: 597 grams

About this title

Synopsis

This book addresses the emergence of Interactive Natural Language Processing (iNLP) and its novel paradigm within the field of NLP and discusses the limitations in existing frameworks while aligning with the ultimate goals of artificial intelligence. This paradigm considers language models as agents capable of observing, acting, and receiving feedback iteratively from external entities.  The book provides a comprehensive survey of iNLP, starting by proposing a unified definition and framework of the concept. In addition, it provides a systematic classification of iNLP and dissects its various components, including interactive objects, interaction interfaces, and interaction methods.  The book then proceeds to delve into the evaluation methodologies used in the field and explores its diverse applications, scrutinizes its ethical and safety issues, and discusses prospective research directions. This work aims to be an entry point for researchers who are interested in this rapidly evolving area and offers a broad view of the current landscape and future trajectory of iNLP.

About the Author

Zekun Wang is a graduate student at Beihang University. Previously, he interned at Beijing Academy of Artificial Intelligence and ByteDance. He is currently at KlingAI from Kuaishou Technology. His research focuses on Large Language Models and Multimodal Large Language Models.

Ge Zhang is currently pursing a Ph.D. degree at the University of Waterloo. His research interests include music generation, self-supervised learning, and natural language processing, specifically human moral value alignment.

Chenghua Lin is a Full Professor and Chair in Natural Language Processing in the Department of Computer Science at the University of Manchester. His research focuses on the integration of machine learning and natural language processing for language generation and understanding. He also serves as Chair of the ACL SIGGEN Board and is a member of the IEEE Speech and Language Processing Technical Committee.

Jie Fu, Ph.D., is a visiting scholar at Hong Kong University of Science and Technology. He obtained his Ph.D. from the National University of Singapore and did his postdoctoral training at Quebec AI Institute (Mila). His reearch focuses on deep learning and large languge models.