Statistical Machine Translation

Statistical Machine Translation
Author: Philipp Koehn
Publisher: Cambridge University Press
Total Pages: 447
Release: 2010
Genre: Computers
ISBN: 0521874157

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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.

Syntax-based Statistical Machine Translation

Syntax-based Statistical Machine Translation
Author: Philip Williams
Publisher: Springer Nature
Total Pages: 190
Release: 2022-05-31
Genre: Computers
ISBN: 3031021649

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This unique book provides a comprehensive introduction to the most popular syntax-based statistical machine translation models, filling a gap in the current literature for researchers and developers in human language technologies. While phrase-based models have previously dominated the field, syntax-based approaches have proved a popular alternative, as they elegantly solve many of the shortcomings of phrase-based models. The heart of this book is a detailed introduction to decoding for syntax-based models. The book begins with an overview of synchronous-context free grammar (SCFG) and synchronous tree-substitution grammar (STSG) along with their associated statistical models. It also describes how three popular instantiations (Hiero, SAMT, and GHKM) are learned from parallel corpora. It introduces and details hypergraphs and associated general algorithms, as well as algorithms for decoding with both tree and string input. Special attention is given to efficiency, including search approximations such as beam search and cube pruning, data structures, and parsing algorithms. The book consistently highlights the strengths (and limitations) of syntax-based approaches, including their ability to generalize phrase-based translation units, their modeling of specific linguistic phenomena, and their function of structuring the search space.

Neural Machine Translation

Neural Machine Translation
Author: Philipp Koehn
Publisher: Cambridge University Press
Total Pages: 409
Release: 2020-06-18
Genre: Computers
ISBN: 1108497322

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

Linguistically Motivated Statistical Machine Translation

Linguistically Motivated Statistical Machine Translation
Author: Deyi Xiong
Publisher: Springer
Total Pages: 159
Release: 2015-02-11
Genre: Language Arts & Disciplines
ISBN: 9812873562

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This book provides a wide variety of algorithms and models to integrate linguistic knowledge into Statistical Machine Translation (SMT). It helps advance conventional SMT to linguistically motivated SMT by enhancing the following three essential components: translation, reordering and bracketing models. It also serves the purpose of promoting the in-depth study of the impacts of linguistic knowledge on machine translation. Finally it provides a systematic introduction of Bracketing Transduction Grammar (BTG) based SMT, one of the state-of-the-art SMT formalisms, as well as a case study of linguistically motivated SMT on a BTG-based platform.

Learning Machine Translation

Learning Machine Translation
Author: Cyril Goutte
Publisher: MIT Press
Total Pages: 329
Release: 2009
Genre: Computers
ISBN: 0262072971

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How Machine Learning can improve machine translation: enabling technologies and new statistical techniques.

Verbmobil: Foundations of Speech-to-Speech Translation

Verbmobil: Foundations of Speech-to-Speech Translation
Author: Wolfgang Wahlster
Publisher: Springer Science & Business Media
Total Pages: 676
Release: 2013-04-17
Genre: Computers
ISBN: 3662042304

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In 1992 it seemed very difficult to answer the question whether it would be possible to develop a portable system for the automatic recognition and translation of spon taneous speech. Previous research work on speech processing had focused on read speech only and international projects aimed at automated text translation had just been terminated without achieving their objectives. Within this context, the German Federal Ministry of Education and Research (BMBF) made a careful analysis of all national and international research projects conducted in the field of speech and language technology before deciding to launch an eight-year basic-research lead project in which research groups were to cooperate in an interdisciplinary and international effort covering the disciplines of computer science, computational linguistics, translation science, signal processing, communi cation science and artificial intelligence. At some point, the project comprised up to 135 work packages with up to 33 research groups working on these packages. The project was controlled by means of a network plan. Every two years the project sit uation was assessed and the project goals were updated. An international scientific advisory board provided advice for BMBF. A new scientific approach was chosen for this project: coping with the com plexity of spontaneous speech with all its pertinent phenomena such as ambiguities, self-corrections, hesitations and disfluencies took precedence over the intended lex icon size. Another important aspect was that prosodic information was exploited at all processing stages.

Readings in Machine Translation

Readings in Machine Translation
Author: Sergei Nirenburg
Publisher: MIT Press
Total Pages: 444
Release: 2003
Genre: Computers
ISBN: 9780262140744

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The field of machine translation (MT) - the automation of translation between human languages - has existed for more than 50 years. MT helped to usher in the field of computational linguistics and has influenced methods and applications in knowledge representation, information theory, and mathematical statistics.

Machine Translation

Machine Translation
Author: Pushpak Bhattacharyya
Publisher: CRC Press
Total Pages: 261
Release: 2015-02-04
Genre: Business & Economics
ISBN: 1439897190

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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.

Machine Learning in Translation Corpora Processing

Machine Learning in Translation Corpora Processing
Author: Krzysztof Wolk
Publisher: CRC Press
Total Pages: 209
Release: 2019-02-25
Genre: Computers
ISBN: 0429588836

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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.