Syntax-based Statistical Machine Translation
Syntax-based Statistical Machine Translation

Author: Philip Williams

Publisher: Springer Nature

Published: 2022-05-31

Total Pages: 190

ISBN-13: 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.

Syntax-based Statistical Machine Translation
Syntax-based Statistical Machine Translation

Author: Philip Williams

Publisher: Morgan & Claypool Publishers

Published: 2016-08-01

Total Pages: 211

ISBN-13: 1627055029

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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.

Syntax-based Statistical Machine Translation
Syntax-based Statistical Machine Translation

Author: Philip Williams

Publisher: Springer

Published: 2016-08-11

Total Pages: 190

ISBN-13: 9783031010361

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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.

Statistical Machine Translation
Statistical Machine Translation

Author: Philipp Koehn

Publisher: Cambridge University Press

Published: 2010

Total Pages: 447

ISBN-13: 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.

Neural Machine Translation
Neural Machine Translation

Author: Philipp Koehn

Publisher: Cambridge University Press

Published: 2020-06-18

Total Pages: 409

ISBN-13: 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.

Advances in Empirical Translation Studies
Advances in Empirical Translation Studies

Author: Meng Ji

Publisher: Cambridge University Press

Published: 2019-06-13

Total Pages: 285

ISBN-13: 1108423272

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Introduces the integration of theoretical and applied translation studies for socially-oriented and data-driven empirical translation research.

Syntax-Based Collocation Extraction
Syntax-Based Collocation Extraction

Author: Violeta Seretan

Publisher: Springer Science & Business Media

Published: 2011-01-04

Total Pages: 222

ISBN-13: 9400701349

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Syntax-Based Collocation Extraction is the first book to offer a comprehensive, up-to-date review of the theoretical and applied work on word collocations. Backed by solid theoretical results, the computational experiments described based on data in four languages provide support for the book’s basic argument for using syntax-driven extraction as an alternative to the current cooccurrence-based extraction techniques to efficiently extract collocational data. The work described in Syntax-Based Collocation Extraction focuses on using linguistic tools for corpus-based identification of collocations. It takes advantage of recent advances in parsing to propose a novel deep syntactic analytic collocation extraction that has applicability to a range of important core tasks in Computational Linguistics. The book is useful for anyone interested in computational analysis of texts, collocation phenomena, and multi-word expressions in general.

Challenges for Arabic Machine Translation
Challenges for Arabic Machine Translation

Author: Abdelhadi Soudi

Publisher: John Benjamins Publishing

Published: 2012-08-01

Total Pages: 167

ISBN-13: 9027273626

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This book is the first volume that focuses on the specific challenges of machine translation with Arabic either as source or target language. It nicely fills a gap in the literature by covering approaches that belong to the three major paradigms of machine translation: Example-based, statistical and knowledge-based. It provides broad but rigorous coverage of the methods for incorporating linguistic knowledge into empirical MT. The book brings together original and extended contributions from a group of distinguished researchers from both academia and industry. It is a welcome and much-needed repository of important aspects in Arabic Machine Translation such as morphological analysis and syntactic reordering, both central to reducing the distance between Arabic and other languages. Most of the proposed techniques are also applicable to machine translation of Semitic languages other than Arabic, as well as translation of other languages with a complex morphology.

Machine Learning in Translation Corpora Processing
Machine Learning in Translation Corpora Processing

Author: Krzysztof Wolk

Publisher: CRC Press

Published: 2019-02-25

Total Pages: 205

ISBN-13: 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.