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Author: Philipp Koehn
Publisher: Cambridge University Press
ISBN: 1108497322
Category : Computers
Languages : en
Pages : 409
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Book Description
Learn how to build machine translation systems with deep learning from the ground up, from basic concepts to cutting-edge research.
Author: Philipp Koehn
Publisher: Cambridge University Press
ISBN: 1108497322
Category : Computers
Languages : en
Pages : 409
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Book Description
Learn how to build machine translation systems with deep learning from the ground up, from basic concepts to cutting-edge research.
Author: Rajesh Arumugam
Publisher: Packt Publishing Ltd
ISBN: 1789135915
Category : Computers
Languages : en
Pages : 307
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Book Description
Foster your NLP applications with the help of deep learning, NLTK, and TensorFlow Key Features Weave neural networks into linguistic applications across various platforms Perform NLP tasks and train its models using NLTK and TensorFlow Boost your NLP models with strong deep learning architectures such as CNNs and RNNs Book Description Natural language processing (NLP) has found its application in various domains, such as web search, advertisements, and customer services, and with the help of deep learning, we can enhance its performances in these areas. Hands-On Natural Language Processing with Python teaches you how to leverage deep learning models for performing various NLP tasks, along with best practices in dealing with today’s NLP challenges. To begin with, you will understand the core concepts of NLP and deep learning, such as Convolutional Neural Networks (CNNs), recurrent neural networks (RNNs), semantic embedding, Word2vec, and more. You will learn how to perform each and every task of NLP using neural networks, in which you will train and deploy neural networks in your NLP applications. You will get accustomed to using RNNs and CNNs in various application areas, such as text classification and sequence labeling, which are essential in the application of sentiment analysis, customer service chatbots, and anomaly detection. You will be equipped with practical knowledge in order to implement deep learning in your linguistic applications using Python's popular deep learning library, TensorFlow. By the end of this book, you will be well versed in building deep learning-backed NLP applications, along with overcoming NLP challenges with best practices developed by domain experts. What you will learn Implement semantic embedding of words to classify and find entities Convert words to vectors by training in order to perform arithmetic operations Train a deep learning model to detect classification of tweets and news Implement a question-answer model with search and RNN models Train models for various text classification datasets using CNN Implement WaveNet a deep generative model for producing a natural-sounding voice Convert voice-to-text and text-to-voice Train a model to convert speech-to-text using DeepSpeech Who this book is for Hands-on Natural Language Processing with Python is for you if you are a developer, machine learning or an NLP engineer who wants to build a deep learning application that leverages NLP techniques. This comprehensive guide is also useful for deep learning users who want to extend their deep learning skills in building NLP applications. All you need is the basics of machine learning and Python to enjoy the book.
Author: Cyril Goutte
Publisher: MIT Press
ISBN: 0262072971
Category : Computers
Languages : en
Pages : 329
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Book Description
How Machine Learning can improve machine translation: enabling technologies and new statistical techniques.
Author: Philipp Koehn
Publisher: Cambridge University Press
ISBN: 0521874157
Category : Computers
Languages : en
Pages : 447
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Book Description
The dream of automatic language translation is now closer thanks to recent advances in the techniques that underpin statistical machine translation. This class-tested textbook from an active researcher in the field, provides a clear and careful introduction to the latest methods and explains how to build machine translation systems for any two languages. It introduces the subject's building blocks from linguistics and probability, then covers the major models for machine translation: word-based, phrase-based, and tree-based, as well as machine translation evaluation, language modeling, discriminative training and advanced methods to integrate linguistic annotation. The book also reports the latest research, presents the major outstanding challenges, and enables novices as well as experienced researchers to make novel contributions to this exciting area. Ideal for students at undergraduate and graduate level, or for anyone interested in the latest developments in machine translation.
Author: Joss Moorkens
Publisher: Springer
ISBN: 3319912410
Category : Computers
Languages : en
Pages : 292
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Book Description
This is the first volume that brings together research and practice from academic and industry settings and a combination of human and machine translation evaluation. Its comprehensive collection of papers by leading experts in human and machine translation quality and evaluation who situate current developments and chart future trends fills a clear gap in the literature. This is critical to the successful integration of translation technologies in the industry today, where the lines between human and machine are becoming increasingly blurred by technology: this affects the whole translation landscape, from students and trainers to project managers and professionals, including in-house and freelance translators, as well as, of course, translation scholars and researchers. The editors have broad experience in translation quality evaluation research, including investigations into professional practice with qualitative and quantitative studies, and the contributors are leading experts in their respective fields, providing a unique set of complementary perspectives on human and machine translation quality and evaluation, combining theoretical and applied approaches.
Author: Yong Cheng
Publisher: Springer Nature
ISBN: 9813297484
Category : Computers
Languages : en
Pages : 90
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Book Description
This book presents four approaches to jointly training bidirectional neural machine translation (NMT) models. First, in order to improve the accuracy of the attention mechanism, it proposes an agreement-based joint training approach to help the two complementary models agree on word alignment matrices for the same training data. Second, it presents a semi-supervised approach that uses an autoencoder to reconstruct monolingual corpora, so as to incorporate these corpora into neural machine translation. It then introduces a joint training algorithm for pivot-based neural machine translation, which can be used to mitigate the data scarcity problem. Lastly it describes an end-to-end bidirectional NMT model to connect the source-to-target and target-to-source translation models, allowing the interaction of parameters between these two directional models.
Author: Sin-wai Chan
Publisher: Routledge
ISBN: 1351376241
Category : Language Arts & Disciplines
Languages : en
Pages : 269
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Book Description
Machine translation has become increasingly popular, especially with the introduction of neural machine translation in major online translation systems. However, despite the rapid advances in machine translation, the role of a human translator remains crucial. As illustrated by the chapters in this book, man-machine interaction is essential in machine translation, localisation, terminology management, and crowdsourcing translation. In fact, the importance of a human translator before, during, and after machine processing, cannot be overemphasised as human intervention is the best way to ensure the translation quality of machine translation. This volume explores the role of a human translator in machine translation from various perspectives, affording a comprehensive look at this topical research area. This book is essential reading for anyone involved in translation studies, machine translation or interested in translation technology.
Author: Sergei Nirenburg
Publisher: IOS Press
ISBN: 9789051990744
Category : Computers
Languages : en
Pages : 338
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Book Description
Author: Yves Gambier
Publisher: John Benjamins Publishing
ISBN: 9027273766
Category : Language Arts & Disciplines
Languages : en
Pages : 470
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Book Description
As a meaningful manifestation of how institutionalized the discipline has become, the new Handbook of Translation Studies is most welcome. The HTS aims at disseminating knowledge about translation and interpreting to a relatively broad audience: not only students who often adamantly prefer user-friendliness, researchers and lecturers in Translation Studies, Translation & Interpreting professionals; but also scholars, experts and professionals from other disciplines (among which linguistics, sociology, history, psychology). Moreover, the HTS is the first handbook with this scope in Translation Studies that has both a print edition and an online version. The HTS is variously searchable: by article, by author, by subject. Another benefit is the interconnection with the selection and organization principles of the online Translation Studies Bibliography (TSB). Many items in the reference lists are hyperlinked to the TSB, where the user can find an abstract of a publication. All articles are written by specialists in the different subfields and are peer-reviewed
Author: Antonio Gulli
Publisher: Packt Publishing Ltd
ISBN: 1788291867
Category : Computers
Languages : en
Pages : 526
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Book Description
Take the next step in implementing various common and not-so-common neural networks with Tensorflow 1.x About This Book Skill up and implement tricky neural networks using Google's TensorFlow 1.x An easy-to-follow guide that lets you explore reinforcement learning, GANs, autoencoders, multilayer perceptrons and more. Hands-on recipes to work with Tensorflow on desktop, mobile, and cloud environment Who This Book Is For This book is intended for data analysts, data scientists, machine learning practitioners and deep learning enthusiasts who want to perform deep learning tasks on a regular basis and are looking for a handy guide they can refer to. People who are slightly familiar with neural networks, and now want to gain expertise in working with different types of neural networks and datasets, will find this book quite useful. What You Will Learn Install TensorFlow and use it for CPU and GPU operations Implement DNNs and apply them to solve different AI-driven problems. Leverage different data sets such as MNIST, CIFAR-10, and Youtube8m with TensorFlow and learn how to access and use them in your code. Use TensorBoard to understand neural network architectures, optimize the learning process, and peek inside the neural network black box. Use different regression techniques for prediction and classification problems Build single and multilayer perceptrons in TensorFlow Implement CNN and RNN in TensorFlow, and use it to solve real-world use cases. Learn how restricted Boltzmann Machines can be used to recommend movies. Understand the implementation of Autoencoders and deep belief networks, and use them for emotion detection. Master the different reinforcement learning methods to implement game playing agents. GANs and their implementation using TensorFlow. In Detail Deep neural networks (DNNs) have achieved a lot of success in the field of computer vision, speech recognition, and natural language processing. The entire world is filled with excitement about how deep networks are revolutionizing artificial intelligence. This exciting recipe-based guide will take you from the realm of DNN theory to implementing them practically to solve the real-life problems in artificial intelligence domain. In this book, you will learn how to efficiently use TensorFlow, Google's open source framework for deep learning. You will implement different deep learning networks such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Q-learning Networks (DQNs), and Generative Adversarial Networks (GANs) with easy to follow independent recipes. You will learn how to make Keras as backend with TensorFlow. With a problem-solution approach, you will understand how to implement different deep neural architectures to carry out complex tasks at work. You will learn the performance of different DNNs on some popularly used data sets such as MNIST, CIFAR-10, Youtube8m, and more. You will not only learn about the different mobile and embedded platforms supported by TensorFlow but also how to set up cloud platforms for deep learning applications. Get a sneak peek of TPU architecture and how they will affect DNN future. By using crisp, no-nonsense recipes, you will become an expert in implementing deep learning techniques in growing real-world applications and research areas such as reinforcement learning, GANs, autoencoders and more. Style and approach This book consists of hands-on recipes where you'll deal with real-world problems. You'll execute a series of tasks as you walk through data mining challenges using TensorFlow 1.x. Your one-stop solution for common and not-so-common pain points, this is a book that you must have on the shelf.
