Learn how to build machine translation systems with deep learning from the ground up, from basic concepts to cutting-edge research.

The purpose of this edited volume is to provide a comprehensive overview on the fundamentals of deep learning, introduce the widely-used learning architectures and algorithms, present its latest theoretical progress, discuss the most popular deep learning platforms and data sets, and describe how many deep learning methodologies have brought great breakthroughs in various applications of text, image, video, speech and audio processing. Deep learning (DL) has been widely considered as the next generation of machine learning methodology. DL attracts much attention and also achieves great success in pattern recognition, computer vision, data mining, and knowledge discovery due to its great capability in learning high-level abstract features from vast amount of data. This new book will not only attempt to provide a general roadmap or guidance to the current deep learning methodologies, but also present the challenges and envision new perspectives which may lead to further breakthroughs in this field. This book will serve as a useful reference for senior (undergraduate or graduate) students in computer science, statistics, electrical engineering, as well as others interested in studying or exploring the potential of exploiting deep learning algorithms. It will also be of special interest to researchers in the area of AI, pattern recognition, machine learning and related areas, alongside engineers interested in applying deep learning models in existing or new practical applications.

Clear and concise, this textbook provides a non-technical introduction to the basic and central concepts of translation theory and practice, including translation briefs, parallel texts and textual functions, cohesion and coherence, and old and new information. Colina focuses on the key concepts that beginning students of translation, practising translators, language students and language professionals need to understand. Numerous exercises (discussion, group and individual) at the end of each chapter and 'Practice' activities throughout each chapter allow students to self-assess their practical understanding of chapter topics. In addition, examples, figures and text extracts from a wide variety of world languages contextualise chapter material and produce a lively and accessible narrative. Suitable for non-specialists with no prior experience of translation, it will also be of interest to practising translators, language students and language industry professionals who wish to gain a wider and up-to-date understanding of translation.

What Is Machine Translation The subfield of computational linguistics known as machine translation, which is often referred to by the abbreviation MT at times, explores the use of software to translate text or speech from one language to another. Machine translation can also be referred to as automatic translation. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Machine Translation Chapter 2: Computational Linguistics Chapter 3: Natural Language Processing Chapter 4: Statistical Machine Translation Chapter 5: Neural Machine Translation Chapter 6: Google Neural Machine Translation Chapter 7: Hybrid Machine Translation Chapter 8: Rule-based Machine Translation Chapter 9: Evaluation of Machine Translation Chapter 10: History of Machine Translation (II) Answering the public top questions about machine translation. (III) Real world examples for the usage of machine translation in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of machine translation' technologies. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of machine translation.

Clear and concise, this textbook provides a non-technical introduction to the basic theory of translation, with numerous examples and exercises.

Machine Learning in Translation introduces machine learning (ML) theories and technologies that are most relevant to translation processes, approaching the topic from a human perspective and emphasizing that ML and ML-driven technologies are tools for humans. Providing an exploration of the common ground between human and machine learning and of the nature of translation that leverages this new dimension, this book helps linguists, translators, and localizers better find their added value in a ML-driven translation environment. Part One explores how humans and machines approach the problem of translation in their own particular ways, in terms of word embeddings, chunking of larger meaning units, and prediction in translation based upon the broader context. Part Two introduces key tasks, including machine translation, translation quality assessment and quality estimation, and other Natural Language Processing (NLP) tasks in translation. Part Three focuses on the role of data in both human and machine learning processes. It proposes that a translator’s unique value lies in the capability to create, manage, and leverage language data in different ML tasks in the translation process. It outlines new knowledge and skills that need to be incorporated into traditional translation education in the machine learning era. The book concludes with a discussion of human-centered machine learning in translation, stressing the need to empower translators with ML knowledge, through communication with ML users, developers, and programmers, and with opportunities for continuous learning. This accessible guide is designed for current and future users of ML technologies in localization workflows, including students on courses in translation and localization, language technology, and related areas. It supports the professional development of translation practitioners, so that they can fully utilize ML technologies and design their own human-centered ML-driven translation workflows and NLP tasks.