Low Rank Rnn Adaptation For Context Aware Language Modeling
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Low Rank Rnn Adaptation For Context Aware Language Modeling
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Author : Aaron Jaech
language : en
Publisher:
Release Date : 2018
Low Rank Rnn Adaptation For Context Aware Language Modeling written by Aaron Jaech and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
A long-standing weakness of statistical language models is that their performance drastically degrades if they are used on data that varies even slightly from the data on which they were trained. In practice, applications require the use of adaptation methods to adjust the predictions of the model to match the local context. For instance, in a speech recognition application, a single static language model would not be able to handle all the different ways that people speak to their voice assistants such as selecting music and sending a message to a friend. An adapted model would make its predictions conditioned on the knowledge of who is speaking and what task they are trying to do. The current standard approach to recurrent neural network language model adaptation is to apply a simple linear shift to the recurrent and/or output layer bias vector. Although this is helpful, it does not go far enough. This thesis introduces a new approach to adaptation, which we call the FactorCell, that generates a custom recurrent network for each context by applying a low-rank transformation. The FactorCell allows for a more substantial change to the recurrent layer weights. Different from previous approaches, the introduction of a rank hyperparameter gives control over how different or similar the adapted models should be. In our experiments on several different datasets and multiple types of context, the increased adaptation of the recurrent layer is always helpful, as measured by perplexity, the standard for evaluating language models. We also demonstrate impact on two applications: personalized query completion and context-specific text generation, finding that the enhanced adaptation benefits both. We also show that the FactorCell provides a more effective text classification model, but more importantly the classification results reveal that there are important differences between the models that are not captured by perplexity. The classification metric is particularly important for the text generation application.
Text Speech And Dialogue
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Author : Kamil Ekštein
language : en
Publisher: Springer Nature
Release Date : 2021-08-30
Text Speech And Dialogue written by Kamil Ekštein and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-30 with Computers categories.
This book constitutes the proceedings of the 24th International Conference on Text, Speech, and Dialogue, TSD 2021, held in Olomouc, Czech Republic, in September 2021.* The 2 keynote speeches and 46 papers presented in this volume were carefully reviewed and selected from 101 submissions. The topical sections "Text", "Speech", and "Dialogue" deal with the following issues: speech recognition; corpora and language resources; speech and spoken language generation; tagging, classification and parsing of text and speech; semantic processing of text and speech; integrating applications of text and speech processing; automatic dialogue systems; multimodal techniques and modelling, and others. * Due to the COVID-19 pandemic the conference was held in a "hybrid" mode.
Supervised Sequence Labelling With Recurrent Neural Networks
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Author : Alex Graves
language : en
Publisher: Springer
Release Date : 2012-02-06
Supervised Sequence Labelling With Recurrent Neural Networks written by Alex Graves and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-02-06 with Technology & Engineering categories.
Supervised sequence labelling is a vital area of machine learning, encompassing tasks such as speech, handwriting and gesture recognition, protein secondary structure prediction and part-of-speech tagging. Recurrent neural networks are powerful sequence learning tools—robust to input noise and distortion, able to exploit long-range contextual information—that would seem ideally suited to such problems. However their role in large-scale sequence labelling systems has so far been auxiliary. The goal of this book is a complete framework for classifying and transcribing sequential data with recurrent neural networks only. Three main innovations are introduced in order to realise this goal. Firstly, the connectionist temporal classification output layer allows the framework to be trained with unsegmented target sequences, such as phoneme-level speech transcriptions; this is in contrast to previous connectionist approaches, which were dependent on error-prone prior segmentation. Secondly, multidimensional recurrent neural networks extend the framework in a natural way to data with more than one spatio-temporal dimension, such as images and videos. Thirdly, the use of hierarchical subsampling makes it feasible to apply the framework to very large or high resolution sequences, such as raw audio or video. Experimental validation is provided by state-of-the-art results in speech and handwriting recognition.
Representation Learning For Natural Language Processing
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Author : Zhiyuan Liu
language : en
Publisher: Springer Nature
Release Date : 2020-07-03
Representation Learning For Natural Language Processing written by Zhiyuan Liu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-03 with Computers categories.
This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for natural language processing (NLP). It is divided into three parts. Part I presents the representation learning techniques for multiple language entries, including words, phrases, sentences and documents. Part II then introduces the representation techniques for those objects that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, networks, and cross-modal entries. Lastly, Part III provides open resource tools for representation learning techniques, and discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing.
Automatic Speech Recognition
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Author : Dong Yu
language : en
Publisher: Springer
Release Date : 2014-11-11
Automatic Speech Recognition written by Dong Yu and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-11-11 with Technology & Engineering categories.
This book provides a comprehensive overview of the recent advancement in the field of automatic speech recognition with a focus on deep learning models including deep neural networks and many of their variants. This is the first automatic speech recognition book dedicated to the deep learning approach. In addition to the rigorous mathematical treatment of the subject, the book also presents insights and theoretical foundation of a series of highly successful deep learning models.
Neural Approaches To Conversational Ai Question Answering Task Oriented Dialogues And Social Chatbots
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Author : Jianfeng Gao
language : en
Publisher: Foundations and Trends(r) in I
Release Date : 2019-02-21
Neural Approaches To Conversational Ai Question Answering Task Oriented Dialogues And Social Chatbots written by Jianfeng Gao and has been published by Foundations and Trends(r) in I this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-02-21 with Computers categories.
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.
Robust Automatic Speech Recognition
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Author : Jinyu Li
language : en
Publisher: Academic Press
Release Date : 2015-10-30
Robust Automatic Speech Recognition written by Jinyu Li and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-10-30 with Technology & Engineering categories.
Robust Automatic Speech Recognition: A Bridge to Practical Applications establishes a solid foundation for automatic speech recognition that is robust against acoustic environmental distortion. It provides a thorough overview of classical and modern noise-and reverberation robust techniques that have been developed over the past thirty years, with an emphasis on practical methods that have been proven to be successful and which are likely to be further developed for future applications.The strengths and weaknesses of robustness-enhancing speech recognition techniques are carefully analyzed. The book covers noise-robust techniques designed for acoustic models which are based on both Gaussian mixture models and deep neural networks. In addition, a guide to selecting the best methods for practical applications is provided.The reader will: Gain a unified, deep and systematic understanding of the state-of-the-art technologies for robust speech recognition Learn the links and relationship between alternative technologies for robust speech recognition Be able to use the technology analysis and categorization detailed in the book to guide future technology development Be able to develop new noise-robust methods in the current era of deep learning for acoustic modeling in speech recognition The first book that provides a comprehensive review on noise and reverberation robust speech recognition methods in the era of deep neural networks Connects robust speech recognition techniques to machine learning paradigms with rigorous mathematical treatment Provides elegant and structural ways to categorize and analyze noise-robust speech recognition techniques Written by leading researchers who have been actively working on the subject matter in both industrial and academic organizations for many years
Federated Learning
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Author : Qiang Qiang Yang
language : en
Publisher: Springer Nature
Release Date : 2022-06-01
Federated Learning written by Qiang Qiang Yang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-01 with Computers categories.
How is it possible to allow multiple data owners to collaboratively train and use a shared prediction model while keeping all the local training data private? Traditional machine learning approaches need to combine all data at one location, typically a data center, which may very well violate the laws on user privacy and data confidentiality. Today, many parts of the world demand that technology companies treat user data carefully according to user-privacy laws. The European Union's General Data Protection Regulation (GDPR) is a prime example. In this book, we describe how federated machine learning addresses this problem with novel solutions combining distributed machine learning, cryptography and security, and incentive mechanism design based on economic principles and game theory. We explain different types of privacy-preserving machine learning solutions and their technological backgrounds, and highlight some representative practical use cases. We show how federated learning can become the foundation of next-generation machine learning that caters to technological and societal needs for responsible AI development and application.
Computer Vision Eccv 2020
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Author : Andrea Vedaldi
language : en
Publisher: Springer Nature
Release Date : 2020-11-26
Computer Vision Eccv 2020 written by Andrea Vedaldi and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-26 with Computers categories.
The 30-volume set, comprising the LNCS books 12346 until 12375, constitutes the refereed proceedings of the 16th European Conference on Computer Vision, ECCV 2020, which was planned to be held in Glasgow, UK, during August 23-28, 2020. The conference was held virtually due to the COVID-19 pandemic. The 1360 revised papers presented in these proceedings were carefully reviewed and selected from a total of 5025 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.
The Probabilistic Relevance Framework
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Author : Stephen Robertson
language : en
Publisher: Now Publishers Inc
Release Date : 2009
The Probabilistic Relevance Framework written by Stephen Robertson and has been published by Now Publishers Inc this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with Computers categories.
The Probabilistic Relevance Framework (PRF) is a formal framework for document retrieval, grounded in work done in the 1970-80s, which led to the development of one of the most successful text-retrieval algorithms, BM25. In recent years, research in the PRF has yielded new retrieval models capable of taking into account structure and link-graph information. Again, this has led to one of the most successful web-search and corporate-search algorithms, BM25F. The Probabilistic Relevance Framework: BM25 and Beyond presents the PRF from a conceptual point of view, describing the probabilistic modelling assumptions behind the framework and the different ranking algorithms that result from its application: the binary independence model, relevance feedback models, BM25, BM25F. Besides presenting a full derivation of the PRF ranking algorithms, it provides many insights about document retrieval in general, and points to many open challenges in this area. It also discusses the relation between the PRF and other statistical models for IR, and covers some related topics, such as the use of non-textual features, and parameter optimization for models with free parameters. The Probabilistic Relevance Framework: BM25 and Beyond is self-contained and accessible to anyone with basic knowledge of probability and inference