1# Library to Find Best eBook for Read
Author : Nicolas Carrara
Release : 2019
Genre :
Kind : eBook
Book Rating : /5 ( reviews)
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. This book was released on 2019. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Verena Rieser
Release : 2011-11-23
Genre : Computers
Kind : eBook
Book Rating : 426/5 ( reviews)
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. This book was released on 2011-11-23. 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.
Author : Oliver Lemon
Release : 2012-10-20
Genre : Computers
Kind : eBook
Book Rating : 034/5 ( reviews)
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. This book was released on 2012-10-20. 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.
Author : Sahisnu Mazumder
Release : 2024-02-09
Genre : Computers
Kind : eBook
Book Rating : 895/5 ( reviews)
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. This book was released on 2024-02-09. 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.
Author : Layla El Asri
Release : 2016
Genre :
Kind : eBook
Book Rating : /5 ( reviews)
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. This book was released on 2016. 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.
