Lifelong and Continual Learning Dialogue Systems

Sahisnu Mazumder · Bing Liu

2024年1月 · Springer Nature

電子書籍

171

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この電子書籍について

This book introduces the new paradigm of lifelong and continual learning dialogue systems to endow dialogue systems with the ability to learn continually by themselves through their own self-initiated interactions with their users and the working environments. The authors present the latest developments and techniques for building such continual learning dialogue systems. The book explains how these developments allow systems to continuously learn new language expressions, lexical and factual knowledge, and conversational skills through interactions and dialogues. Additionally, the book covers techniques to acquire new training examples for learning new tasks during the conversation. The book also reviews existing work on lifelong learning and discusses areas for future research.

著者について

Sahisnu Mazumder is an AI Research Scientist at Intel Labs, USA, where he works on human-AI collaboration and dialogue and interactive systems research. He obtained his Ph.D. in Computer Science at the University of Illinois at Chicago, USA, and his Masters in Computer Science from the Indian Institute of Technology (IIT), Roorkee, India. His research interests include lifelong and continual learning, dialogue and interactive systems, open-world AI and learning, knowledge base reasoning, and sentiment analysis. He has published several research papers in these areas in leading AI, NLP and Dialogue conferences and given two tutorial talks on this book topic. During his Ph.D,, he also worked as a Research Intern at Microsoft Research Redmond and Huawei Research USA on virtual AI Assistants.

Bing Liu is a Distinguished Professor of Computer Science at the University of Illinois at Chicago, USA. He received his Ph.D. in Artificial Intelligence from the University of Edinburgh, UK. His research interests include lifelong and continual learning, lifelong learning dialogue systems, open-world learning, sentiment analysis and opinion mining, machine learning, and natural language processing. He has published extensively in top conferences and journals in these areas and has authored four books. Three of his papers have received Test-of-Time awards and another received Test-of-Time honorable mention. He is the winner of 2018 ACM SIGKDD Innovation Award and is a Fellow of AAAI, ACM, and IEEE.