Learning Machine Translation PDF Download
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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: 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: 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: Krzysztof Wolk
Publisher: CRC Press
ISBN: 0429588836
Category : Computers
Languages : en
Pages : 209
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Book Description
This book reviews ways to improve statistical machine speech translation between Polish and English. Research has been conducted mostly on dictionary-based, rule-based, and syntax-based, machine translation techniques. Most popular methodologies and tools are not well-suited for the Polish language and therefore require adaptation, and language resources are lacking in parallel and monolingual data. The main objective of this volume to develop an automatic and robust Polish-to-English translation system to meet specific translation requirements and to develop bilingual textual resources by mining comparable corpora.
Author: Thierry Poibeau
Publisher: MIT Press
ISBN: 0262534215
Category : Computers
Languages : en
Pages : 298
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Book Description
A concise, nontechnical overview of the development of machine translation, including the different approaches, evaluation issues, and major players in the industry. The dream of a universal translation device goes back many decades, long before Douglas Adams's fictional Babel fish provided this service in The Hitchhiker's Guide to the Galaxy. Since the advent of computers, research has focused on the design of digital machine translation tools—computer programs capable of automatically translating a text from a source language to a target language. This has become one of the most fundamental tasks of artificial intelligence. This volume in the MIT Press Essential Knowledge series offers a concise, nontechnical overview of the development of machine translation, including the different approaches, evaluation issues, and market potential. The main approaches are presented from a largely historical perspective and in an intuitive manner, allowing the reader to understand the main principles without knowing the mathematical details. The book begins by discussing problems that must be solved during the development of a machine translation system and offering a brief overview of the evolution of the field. It then takes up the history of machine translation in more detail, describing its pre-digital beginnings, rule-based approaches, the 1966 ALPAC (Automatic Language Processing Advisory Committee) report and its consequences, the advent of parallel corpora, the example-based paradigm, the statistical paradigm, the segment-based approach, the introduction of more linguistic knowledge into the systems, and the latest approaches based on deep learning. Finally, it considers evaluation challenges and the commercial status of the field, including activities by such major players as Google and Systran.
Author: Bonnie Jean Dorr
Publisher: MIT Press
ISBN: 9780262041386
Category : Computers
Languages : en
Pages : 466
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Book Description
This book describes a novel, cross-linguistic approach to machine translation that solves certain classes of syntactic and lexical divergences by means of a lexical conceptual structure that can be composed and decomposed in language-specific ways. This approach allows the translator to operate uniformly across many languages, while still accounting for knowledge that is specific to each language.
Author: Peng Wang
Publisher: Taylor & Francis
ISBN: 100083865X
Category : Language Arts & Disciplines
Languages : en
Pages : 219
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Book Description
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.
Author: Pushpak Bhattacharyya
Publisher: CRC Press
ISBN: 1439897190
Category : Business & Economics
Languages : en
Pages : 261
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Book Description
This book compares and contrasts the principles and practices of rule-based machine translation (RBMT), statistical machine translation (SMT), and example-based machine translation (EBMT). Presenting numerous examples, the text introduces language divergence as the fundamental challenge to machine translation, emphasizes and works out word alignment, explores IBM models of machine translation, covers the mathematics of phrase-based SMT, provides complete walk-throughs of the working of interlingua-based and transfer-based RBMT, and analyzes EBMT, showing how translation parts can be extracted and recombined to automatically translate a new input.
Author: William John Hutchins
Publisher:
ISBN:
Category : Computers
Languages : en
Pages : 392
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Book Description
The translation of foreign language texts by computers was one of the first tasks that the pioneers of computing and artificial intelligence set themselves. Machine translation is again becoming an important field of research and development as the need for translations of technical and commercial documentation is growing beyond the capacity of the translation profession.
Author: Yorick Wilks
Publisher: Springer Science & Business Media
ISBN: 0387727744
Category : Computers
Languages : en
Pages : 246
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Book Description
A history of machine translation (MT) from the point of view of a major writer and innovator in the field is the subject of this book. It details the deep differences between rival groups on how best to do MT, and presents a global perspective covering historical and contemporary systems in Europe, the US and Japan. The author considers MT as a fundamental part of Artificial Intelligence and the ultimate test-bed for all computational linguistics.
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.
