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Reinforcement Learning For Adaptive Dialogue Systems
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Book Synopsis Reinforcement Learning for Adaptive Dialogue Systems by : Verena Rieser
Download or read book Reinforcement Learning for Adaptive Dialogue Systems written by Verena Rieser and published by Springer Science & Business Media. This book was released on 2011-11-23 with total page 261 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Learning the Parameters of Reinforcement Learning from Data for Adaptive Spoken Dialogue Systems by : Layla El Asri
Download or read book Learning the Parameters of Reinforcement Learning from Data for Adaptive Spoken Dialogue Systems written by Layla El Asri and published by . This book was released on 2016 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This document proposes to learn the behaviour of the dialogue manager of a spoken dialogue system from a set of rated dialogues. This learning is performed through reinforcement learning. Our method does not require the definition of a representation of the state space nor a reward function. These two high-level parameters are learnt from the corpus of rated dialogues. It is shown that the spoken dialogue designer can optimise dialogue management by simply defining the dialogue logic and a criterion to maximise (e.g user satisfaction). The methodology suggested in this thesis first considers the dialogue parameters that are necessary to compute a representation of the state space relevant for the criterion to be maximized. For instance, if the chosen criterion is user satisfaction then it is important to account for parameters such as dialogue duration and the average speech recognition confidence score. The state space is represented as a sparse distributed memory. The Genetic Sparse Distributed Memory for Reinforcement Learning (GSDMRL) accommodates many dialogue parameters and selects the parameters which are the most important for learning through genetic evolution. The resulting state space and the policy learnt on it are easily interpretable by the system designer. Secondly, the rated dialogues are used to learn a reward function which teaches the system to optimise the criterion. Two algorithms, reward shaping and distance minimisation are proposed to learn the reward function. These two algorithms consider the criterion to be the return for the entire dialogue. These functions are discussed and compared on simulated dialogues and it is shown that the resulting functions enable faster learning than using the criterion directly as the final reward. A spoken dialogue system for appointment scheduling was designed during this thesis, based on previous systems, and a corpus of rated dialogues with this system were collected. This corpus illustrates the scaling capability of the state space representation and is a good example of an industrial spoken dialogue system upon which the methodology could be applied.
Book Synopsis Data-Driven Methods for Adaptive Spoken Dialogue Systems by : Oliver Lemon
Download or read book Data-Driven Methods for Adaptive Spoken Dialogue Systems written by Oliver Lemon and published by Springer Science & Business Media. This book was released on 2012-10-20 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Towards Adaptive Spoken Dialog Systems by : Alexander Schmitt
Download or read book Towards Adaptive Spoken Dialog Systems written by Alexander Schmitt and published by Springer Science & Business Media. This book was released on 2012-09-19 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: In Monitoring Adaptive Spoken Dialog Systems, authors Alexander Schmitt and Wolfgang Minker investigate statistical approaches that allow for recognition of negative dialog patterns in Spoken Dialog Systems (SDS). The presented stochastic methods allow a flexible, portable and accurate use. Beginning with the foundations of machine learning and pattern recognition, this monograph examines how frequently users show negative emotions in spoken dialog systems and develop novel approaches to speech-based emotion recognition using hybrid approach to model emotions. The authors make use of statistical methods based on acoustic, linguistic and contextual features to examine the relationship between the interaction flow and the occurrence of emotions using non-acted recordings several thousand real users from commercial and non-commercial SDS. Additionally, the authors present novel statistical methods that spot problems within a dialog based on interaction patterns. The approaches enable future SDS to offer more natural and robust interactions. This work provides insights, lessons and inspiration for future research and development, not only for spoken dialog systems, but for data-driven approaches to human-machine interaction in general.
Book Synopsis Reinforcement Learning for Dialogue Systems Optimization with User Adaptation by : Nicolas Carrara
Download or read book Reinforcement Learning for Dialogue Systems Optimization with User Adaptation written by Nicolas Carrara and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Natural Language Dialog Systems and Intelligent Assistants by : G.G. Lee
Download or read book Natural Language Dialog Systems and Intelligent Assistants written by G.G. Lee and published by Springer. This book was released on 2015-09-28 with total page 269 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers state-of-the-art topics on the practical implementation of Spoken Dialog Systems and intelligent assistants in everyday applications. It presents scientific achievements in language processing that result in the development of successful applications and addresses general issues regarding the advances in Spoken Dialog Systems with applications in robotics, knowledge access and communication. Emphasis is placed on the following topics: speaker/language recognition, user modeling / simulation, evaluation of dialog system, multi-modality / emotion recognition from speech, speech data mining, language resource and databases, machine learning for spoken dialog systems and educational and healthcare applications.
Book Synopsis Lifelong and Continual Learning Dialogue Systems by : Sahisnu Mazumder
Download or read book Lifelong and Continual Learning Dialogue Systems written by Sahisnu Mazumder and published by Springer Nature. This book was released on 2024-02-09 with total page 180 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis A Framework for Unsupervised Learning of Dialogue Strategies by : Olivier Pietquin
Download or read book A Framework for Unsupervised Learning of Dialogue Strategies written by Olivier Pietquin and published by Presses univ. de Louvain. This book was released on 2005-08 with total page 247 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book addresses the problems of spoken dialogue system design and especially automatic learning of optimal strategies for man-machine dialogues. Besides the description of the learning methods, this text proposes a framework for realistic simulation of human-machine dialogues based on probabilistic techniques, which allows automatic evaluation and unsupervised learning of dialogue strategies. This framework relies on stochastic modelling of modules composing spoken dialogue systems as well as on user modelling. Special care has been taken to build models that can either be hand-tuned or learned from generic data.
Book Synopsis Hierarchical Reinforcement Learning for Spoken Dialogue Systems by : Heriberto Cuayáhuitl
Download or read book Hierarchical Reinforcement Learning for Spoken Dialogue Systems written by Heriberto Cuayáhuitl and published by . This book was released on 2009 with total page 203 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis focuses on the problem of scalable optimization of dialogue behaviour in speech-based conversational systems using reinforcement learning. Most previous investigations in dialogue strategy learning have proposed flat reinforcement learning methods, which are more suitable for small-scale spoken dialogue systems. This research formulates the problem in terms of Semi-Markov Decision Processes (SMDPs), and proposes two hierarchical reinforcement learning methods to optimize sub-dialogues rather than full dialogues. The first method uses a hierarchy of SMDPs, where every SMDP ignores irrelevant state variables and actions in order to optimize a sub-dialogue. The second method extends the first one by constraining every SMDP in the hierarchy with prior expert knowledge. The latter method proposes a learning algorithm called 'HAM+HSMQ-Learning', which combines two existing algorithms in the literature of hierarchical reinforcement learning. Whilst the first method generates fully-learnt behaviour, the second one generates semi-learnt behaviour. In addition, this research proposes a heuristic dialogue simulation environment for automatic dialogue strategy learning. Experiments were performed on simulated and real environments based on a travel planning spoken dialogue system. Experimental results provided evidence to support the following claims: First, both methods scale well at the cost of near-optimal solutions, resulting in slightly longer dialogues than the optimal solutions. Second, dialogue strategies learnt with coherent user behaviour and conservative recognition error rates can outperform a reasonable hand-coded strategy. Third, semi-learnt dialogue behaviours are a better alternative (because of their higher overall performance) than hand-coded or fully-learnt dialogue behaviours. Last, hierarchical reinforcement learning dialogue agents are feasible and promising for the (semi) automatic design of adaptive behaviours in larger-scale spoken dialogue systems. This research makes the following contributions to spoken dialogue systems which learn their dialogue behaviour. First, the Semi-Markov Decision Process (SMDP) model was proposed to learn spoken dialogue strategies in a scalable way. Second, the concept of 'partially specified dialogue strategies' was proposed for integrating simultaneously hand-coded and learnt spoken dialogue behaviours into a single learning framework. Third, an evaluation with real users of hierarchical reinforcement learning dialogue agents was essential to validate their effectiveness in a realistic environment.
Book Synopsis Data-Driven Methods for Adaptive Spoken Dialogue Systems by : Oliver Lemon
Download or read book Data-Driven Methods for Adaptive Spoken Dialogue Systems written by Oliver Lemon and published by Springer. This book was released on 2012-10-21 with total page 178 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Natural Language Generation in Interactive Systems by : Amanda Stent
Download or read book Natural Language Generation in Interactive Systems written by Amanda Stent and published by Cambridge University Press. This book was released on 2014-06-12 with total page 383 pages. Available in PDF, EPUB and Kindle. Book excerpt: An informative and comprehensive overview of the state-of-the-art in natural language generation (NLG) for interactive systems, this guide serves to introduce graduate students and new researchers to the field of natural language processing and artificial intelligence, while inspiring them with ideas for future research. Detailing the techniques and challenges of NLG for interactive applications, it focuses on the research into systems that model collaborativity and uncertainty, are capable of being scaled incrementally, and can engage with the user effectively. A range of real-world case studies is also included. The book and the accompanying website feature a comprehensive bibliography, and refer the reader to corpora, data, software and other resources for pursuing research on natural language generation and interactive systems, including dialog systems, multimodal interfaces and assistive technologies. It is an ideal resource for students and researchers in computational linguistics, natural language processing and related fields.
Book Synopsis Building Dialogue POMDPs from Expert Dialogues by : Hamidreza Chinaei
Download or read book Building Dialogue POMDPs from Expert Dialogues written by Hamidreza Chinaei and published by Springer. This book was released on 2016-02-08 with total page 123 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses the Partially Observable Markov Decision Process (POMDP) framework applied in dialogue systems. It presents POMDP as a formal framework to represent uncertainty explicitly while supporting automated policy solving. The authors propose and implement an end-to-end learning approach for dialogue POMDP model components. Starting from scratch, they present the state, the transition model, the observation model and then finally the reward model from unannotated and noisy dialogues. These altogether form a significant set of contributions that can potentially inspire substantial further work. This concise manuscript is written in a simple language, full of illustrative examples, figures, and tables.
Book Synopsis Computational Linguistics and Intelligent Text Processing by : Alexander Gelbukh
Download or read book Computational Linguistics and Intelligent Text Processing written by Alexander Gelbukh and published by Springer. This book was released on 2014-04-18 with total page 554 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set, consisting of LNCS 8403 and LNCS 8404, constitutes the thoroughly refereed proceedings of the 14th International Conference on Intelligent Text Processing and Computational Linguistics, CICLing 2014, held in Kathmandu, Nepal, in April 2014. The 85 revised papers presented together with 4 invited papers were carefully reviewed and selected from 300 submissions. The papers are organized in the following topical sections: lexical resources; document representation; morphology, POS-tagging, and named entity recognition; syntax and parsing; anaphora resolution; recognizing textual entailment; semantics and discourse; natural language generation; sentiment analysis and emotion recognition; opinion mining and social networks; machine translation and multilingualism; information retrieval; text classification and clustering; text summarization; plagiarism detection; style and spelling checking; speech processing; and applications.
Book Synopsis Spoken Dialogue Systems by : Kristiina Jokinen
Download or read book Spoken Dialogue Systems written by Kristiina Jokinen and published by Morgan & Claypool Publishers. This book was released on 2010 with total page 151 pages. Available in PDF, EPUB and Kindle. Book excerpt: Considerable progress has been made in recent years in the development of dialogue systems that support robust and efficient human-machine interaction using spoken language. Spoken dialogue technology allows various interactive applications to be built and used for practical purposes, and research focuses on issues that aim to increase the system's communicative competence by including aspects of error correction, cooperation, multimodality, and adaptation in context. This book gives a comprehensive view of state-of-the-art techniques that are used to build spoken dialogue systems. It provides an overview of the basic issues such as system architectures, various dialogue management methods, system evaluation, and also surveys advanced topics concerning extensions of the basic model to more conversational setups. The goal of the book is to provide an introduction to the methods, problems, and solutions that are used in dialogue system development and evaluation. It presents dialogue modelling and system development issues relevant in both academic and industrial environments and also discusses requirements and challenges for advanced interaction management and future research. Table of Contents: Preface / Introduction to Spoken Dialogue Systems / Dialogue Management / Error Handling / Case Studies: Advanced Approaches to Dialogue Management / Advanced Issues / Methodologies and Practices of Evaluation / Future Directions / References / Author Biographies
Book Synopsis Reinforcement Learning as a Strategy for Engaging Task-oriented Dialogue Systems by : Viet Cuong Nguyen
Download or read book Reinforcement Learning as a Strategy for Engaging Task-oriented Dialogue Systems written by Viet Cuong Nguyen and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Constructive Dialogue Modelling by : Kristiina Jokinen
Download or read book Constructive Dialogue Modelling written by Kristiina Jokinen and published by John Wiley & Sons. This book was released on 2009-05-27 with total page 178 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dialogue management technology has developed rapidly over the years resulting in real-time applications like telephony directories, timetable enquiries, and in-car applications. However, the current technology is still largely based on models that use rigid command language type interactions, and the users need to adapt their human communication strategies to the needs of the technology. As an increasing number of interactive ubiquitous applications will appear, challenges for interaction technology concern especially natural, more human-friendly communication. Recent research has focused on developing speech-based interactive systems that aim to increase the system’s communicative competence. By including aspects of interaction beyond simple speech recognition and question-answer based interaction, applications with more conversational interfaces have become possible. New dialogue management technology needs to address the challenges in human-technology interaction, so that smart environments should not only enable user-controlled command interfaces but equip applications with a capability that affords easy and friendly interactions with the user. Dialogue Modelling: Speech Interaction and Rational Agents provides an overview of the current dialogue technology and research trends in spoken dialogue systems, presenting a coherent perspective of AI-based cooperative interaction management. The book complements existing research regarding human-computer interfaces, speech and language technology, and communication studies in general, bringing different view-points together and integrating them into a single point of reference. Constructive Dialogue Modelling: Presents a guide to spoken dialogue technology and current research trends. Provides an overview of human factors in dialogue systems and delivers a new metaphor for human-computer interaction and computer as agent. Explains the architecture of dialogue systems using examples from systems such as Interact and DUMAS Offers a comprehensive overview of original research into the new trends in speech dialogue technology in light of innovations such as ubiquitous computing. This book will provide essential reading for industrial designers and interface engineers, university researchers and teachers, computer scientists, human communication researchers, speech and language technologists, cognitive engineers/cognitive scientists, as well as social and media researchers, and psychologists. Advanced students and researchers in computer science, speech and language technologies, psychology and communication research will find this text of interest.
Book Synopsis Bayesian Reinforcement Learning for POMDP-based Dialogue Systems by : ShaoWei Png
Download or read book Bayesian Reinforcement Learning for POMDP-based Dialogue Systems written by ShaoWei Png and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Spoken dialogue systems are gaining popularity with improvements in speech recognition technologies. Dialogue systems have been modeled effectively using Partially observable Markov decision processes (POMDPs), achieving improvements in robustness. However, past research on POMDP-based dialogue systems usually assumes that the model parameters are known. This limitation can be addressed through model-based Bayesian reinforcement learning, which offers a rich framework for simultaneous learning and planning. However, due to the high complexity of the framework, a major challenge is to scale up these algorithms for complex dialogue systems. In this work, we show that by exploiting certain known components of the system, such as knowledge of symmetrical properties, and using an approximate on-line planning algorithm, we are able to apply Bayesian RL on several realistic spoken dialogue system domains. We consider several experimental domains. First, a small ...
