Reinforcement Learning For Dialogue Systems Optimization With User Adaptation
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| Author | : Jianfeng Gao |
| Publisher | : Foundations and Trends(r) in I |
| Total Pages | : 184 |
| Release | : 2019-02-21 |
| Genre | : Computers |
| ISBN | : 9781680835526 |
This monograph is the first survey of neural approaches to conversational AI that targets Natural Language Processing and Information Retrieval audiences. It provides a comprehensive survey of the neural approaches to conversational AI that have been developed in the last few years, covering QA, task-oriented and social bots with a unified view of optimal decision making.The authors draw connections between modern neural approaches and traditional approaches, allowing readers to better understand why and how the research has evolved and to shed light on how they can move forward. They also present state-of-the-art approaches to training dialogue agents using both supervised and reinforcement learning. Finally, the authors sketch out the landscape of conversational systems developed in the research community and released in industry, demonstrating via case studies the progress that has been made and the challenges that are still being faced.Neural Approaches to Conversational AI is a valuable resource for students, researchers, and software developers. It provides a unified view, as well as a detailed presentation of the important ideas and insights needed to understand and create modern dialogue agents that will be instrumental to making world knowledge and services accessible to millions of users in ways that seem natural and intuitive.
| Author | : Verena Rieser |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 261 |
| Release | : 2011-11-23 |
| Genre | : Computers |
| ISBN | : 3642249426 |
The past decade has seen a revolution in the field of spoken dialogue systems. As in other areas of Computer Science and Artificial Intelligence, data-driven methods are now being used to drive new methodologies for system development and evaluation. This book is a unique contribution to that ongoing change. A new methodology for developing spoken dialogue systems is described in detail. The journey starts and ends with human behaviour in interaction, and explores methods for learning from the data, for building simulation environments for training and testing systems, and for evaluating the results. The detailed material covers: Spoken and Multimodal dialogue systems, Wizard-of-Oz data collection, User Simulation methods, Reinforcement Learning, and Evaluation methodologies. The book is a research guide for students and researchers with a background in Computer Science, AI, or Machine Learning. It navigates through a detailed case study in data-driven methods for development and evaluation of spoken dialogue systems. Common challenges associated with this approach are discussed and example solutions are provided. This work provides insights, lessons, and inspiration for future research and development – not only for spoken dialogue systems in particular, but for data-driven approaches to human-machine interaction in general.
| Author | : Oliver Lemon |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 184 |
| Release | : 2012-10-20 |
| Genre | : Computers |
| ISBN | : 1461448034 |
Data driven methods have long been used in Automatic Speech Recognition (ASR) and Text-To-Speech (TTS) synthesis and have more recently been introduced for dialogue management, spoken language understanding, and Natural Language Generation. Machine learning is now present “end-to-end” in Spoken Dialogue Systems (SDS). However, these techniques require data collection and annotation campaigns, which can be time-consuming and expensive, as well as dataset expansion by simulation. In this book, we provide an overview of the current state of the field and of recent advances, with a specific focus on adaptivity.
| Author | : Richard S. Sutton |
| Publisher | : MIT Press |
| Total Pages | : 549 |
| Release | : 2018-11-13 |
| Genre | : Computers |
| ISBN | : 0262352702 |
The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.
| Author | : Emiel Krahmer |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 363 |
| Release | : 2010-09-09 |
| Genre | : Computers |
| ISBN | : 3642155723 |
Natural language generation (NLG) is a subfield of natural language processing (NLP) that is often characterized as the study of automatically converting non-linguistic representations (e.g., from databases or other knowledge sources) into coherent natural language text. In recent years the field has evolved substantially. Perhaps the most important new development is the current emphasis on data-oriented methods and empirical evaluation. Progress in related areas such as machine translation, dialogue system design and automatic text summarization and the resulting awareness of the importance of language generation, the increasing availability of suitable corpora in recent years, and the organization of shared tasks for NLG, where different teams of researchers develop and evaluate their algorithms on a shared, held out data set have had a considerable impact on the field, and this book offers the first comprehensive overview of recent empirically oriented NLG research.
| Author | : Arvin Agah |
| Publisher | : Springer |
| Total Pages | : 1038 |
| Release | : 2016-10-06 |
| Genre | : Computers |
| ISBN | : 3319474375 |
This book constitutes the refereed proceedings of the 8th International Conference on Social Robotics, ICSR 2016, held in Kansas City, MO, USA, in November 2016. The 98 revised full papers presented were carefully reviewed and selected from 107 submissions. The theme of the 2016 conference is Sociorobotics: Design and implementation of social behaviors of robots interacting with each other and humans. In addition to technical sessions, ICSR 2016 included three workshops: The Synthetic Method in Social Robotics (SMSR 2016), Social Robots: A Tool to Advance Interventions for Autism, and Using Social Robots to Improve the Quality of Life in the Elderly.
| Author | : Michael McTear |
| Publisher | : Springer Nature |
| Total Pages | : 234 |
| Release | : 2022-05-31 |
| Genre | : Computers |
| ISBN | : 3031021762 |
This book provides a comprehensive introduction to Conversational AI. While the idea of interacting with a computer using voice or text goes back a long way, it is only in recent years that this idea has become a reality with the emergence of digital personal assistants, smart speakers, and chatbots. Advances in AI, particularly in deep learning, along with the availability of massive computing power and vast amounts of data, have led to a new generation of dialogue systems and conversational interfaces. Current research in Conversational AI focuses mainly on the application of machine learning and statistical data-driven approaches to the development of dialogue systems. However, it is important to be aware of previous achievements in dialogue technology and to consider to what extent they might be relevant to current research and development. Three main approaches to the development of dialogue systems are reviewed: rule-based systems that are handcrafted using best practice guidelines; statistical data-driven systems based on machine learning; and neural dialogue systems based on end-to-end learning. Evaluating the performance and usability of dialogue systems has become an important topic in its own right, and a variety of evaluation metrics and frameworks are described. Finally, a number of challenges for future research are considered, including: multimodality in dialogue systems, visual dialogue; data efficient dialogue model learning; using knowledge graphs; discourse and dialogue phenomena; hybrid approaches to dialogue systems development; dialogue with social robots and in the Internet of Things; and social and ethical issues.
| Author | : Baihan Lin |
| Publisher | : Springer Nature |
| Total Pages | : 205 |
| Release | : |
| Genre | : |
| ISBN | : 3031537203 |
| Author | : Amit Kumar |
| Publisher | : Springer Nature |
| Total Pages | : 1425 |
| Release | : |
| Genre | : |
| ISBN | : 9819780438 |
| Author | : Ling Zhenhua |
| Publisher | : Springer Nature |
| Total Pages | : 342 |
| Release | : 2023-05-09 |
| Genre | : Computers |
| ISBN | : 9819924014 |
This book constitutes the refereed proceedings of the 17th National Conference on Man–Machine Speech Communication, NCMMSC 2022, held in China, in December 2022. The 21 full papers and 7 short papers included in this book were carefully reviewed and selected from 108 submissions. They were organized in topical sections as follows: MCPN: A Multiple Cross-Perception Network for Real-Time Emotion Recognition in Conversation.- Baby Cry Recognition Based on Acoustic Segment Model, MnTTS2 An Open-Source Multi-Speaker Mongolian Text-to-Speech Synthesis Dataset.
