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Machine Translation and Transliteration Involving Related and Low-resource Languages

Author: Anoop Kunchukuttan

Publisher: Chapman & Hall/CRC

Published: 2021-08-12

Total Pages: 0

ISBN-13: 9781003096771

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Machine Translation and Transliteration involving Related, Low-resource Languages discusses an important aspect of natural language processing that has received lesser attention: translation and transliteration involving related languages in a low-resource setting. This is a very relevant real-world scenario for people living in neighbouring states/provinces/countries who speak similar languages and need to communicate with each other, but training data to build supporting MT systems is limited. The book discusses different characteristics of related languages with rich examples and draws connections between two problems: translation for related languages and transliteration. It shows how linguistic similarities can be utilized to learn MT systems for related languages with limited data. It comprehensively discusses the use of subword-level models and multilinguality to utilize these linguistic similarities. The second part of the book explores methods for machine transliteration involving related languages based on multilingual and unsupervised approaches. Through extensive experiments over a wide variety of languages, the efficacy of these methods is established. Features Novel methods for machine translation and transliteration between related languages, supported with experiments on a wide variety of languages. An overview of past literature on machine translation for related languages. A case study about machine translation for related languages between 10 major languages from India, which is one of the most linguistically diverse country in the world. The book presents important concepts and methods for machine translation involving related languages. In general, it serves as a good reference to NLP for related languages. It is intended for students, researchers and professionals interested in Machine Translation, Translation Studies, Multilingual Computing Machine and Natural Language Processing. It can be used as reference reading for courses in NLP and machine translation. Anoop Kunchukuttan is a Senior Applied Researcher at Microsoft India. His research spans various areas on multilingual and low-resource NLP. Pushpak Bhattacharyya is a Professor at the Department of Computer Science, IIT Bombay. His research areas are Natural Language Processing, Machine Learning and AI (NLP-ML-AI). Prof. Bhattacharyya has published more than 350 research papers in various areas of NLP.

Book Synopsis Machine Translation and Transliteration Involving Related and Low-resource Languages by : Anoop Kunchukuttan

Download or read book Machine Translation and Transliteration Involving Related and Low-resource Languages written by Anoop Kunchukuttan and published by Chapman & Hall/CRC. This book was released on 2021-08-12 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Translation and Transliteration involving Related, Low-resource Languages discusses an important aspect of natural language processing that has received lesser attention: translation and transliteration involving related languages in a low-resource setting. This is a very relevant real-world scenario for people living in neighbouring states/provinces/countries who speak similar languages and need to communicate with each other, but training data to build supporting MT systems is limited. The book discusses different characteristics of related languages with rich examples and draws connections between two problems: translation for related languages and transliteration. It shows how linguistic similarities can be utilized to learn MT systems for related languages with limited data. It comprehensively discusses the use of subword-level models and multilinguality to utilize these linguistic similarities. The second part of the book explores methods for machine transliteration involving related languages based on multilingual and unsupervised approaches. Through extensive experiments over a wide variety of languages, the efficacy of these methods is established. Features Novel methods for machine translation and transliteration between related languages, supported with experiments on a wide variety of languages. An overview of past literature on machine translation for related languages. A case study about machine translation for related languages between 10 major languages from India, which is one of the most linguistically diverse country in the world. The book presents important concepts and methods for machine translation involving related languages. In general, it serves as a good reference to NLP for related languages. It is intended for students, researchers and professionals interested in Machine Translation, Translation Studies, Multilingual Computing Machine and Natural Language Processing. It can be used as reference reading for courses in NLP and machine translation. Anoop Kunchukuttan is a Senior Applied Researcher at Microsoft India. His research spans various areas on multilingual and low-resource NLP. Pushpak Bhattacharyya is a Professor at the Department of Computer Science, IIT Bombay. His research areas are Natural Language Processing, Machine Learning and AI (NLP-ML-AI). Prof. Bhattacharyya has published more than 350 research papers in various areas of NLP.

Machine Translation and Transliteration involving Related, Low-resource Languages

Author: Anoop Kunchukuttan

Publisher: CRC Press

Published: 2021-09-08

Total Pages: 215

ISBN-13: 1000422410

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Book Synopsis Machine Translation and Transliteration involving Related, Low-resource Languages by : Anoop Kunchukuttan

Download or read book Machine Translation and Transliteration involving Related, Low-resource Languages written by Anoop Kunchukuttan and published by CRC Press. This book was released on 2021-09-08 with total page 215 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Translation and Transliteration involving Related, Low-resource Languages discusses an important aspect of natural language processing that has received lesser attention: translation and transliteration involving related languages in a low-resource setting. This is a very relevant real-world scenario for people living in neighbouring states/provinces/countries who speak similar languages and need to communicate with each other, but training data to build supporting MT systems is limited. The book discusses different characteristics of related languages with rich examples and draws connections between two problems: translation for related languages and transliteration. It shows how linguistic similarities can be utilized to learn MT systems for related languages with limited data. It comprehensively discusses the use of subword-level models and multilinguality to utilize these linguistic similarities. The second part of the book explores methods for machine transliteration involving related languages based on multilingual and unsupervised approaches. Through extensive experiments over a wide variety of languages, the efficacy of these methods is established. Features Novel methods for machine translation and transliteration between related languages, supported with experiments on a wide variety of languages. An overview of past literature on machine translation for related languages. A case study about machine translation for related languages between 10 major languages from India, which is one of the most linguistically diverse country in the world. The book presents important concepts and methods for machine translation involving related languages. In general, it serves as a good reference to NLP for related languages. It is intended for students, researchers and professionals interested in Machine Translation, Translation Studies, Multilingual Computing Machine and Natural Language Processing. It can be used as reference reading for courses in NLP and machine translation. Anoop Kunchukuttan is a Senior Applied Researcher at Microsoft India. His research spans various areas on multilingual and low-resource NLP. Pushpak Bhattacharyya is a Professor at the Department of Computer Science, IIT Bombay. His research areas are Natural Language Processing, Machine Learning and AI (NLP-ML-AI). Prof. Bhattacharyya has published more than 350 research papers in various areas of NLP.

Machine Translation and Transliteration involving Related, Low-resource Languages

Author: Anoop Kunchukuttan

Publisher: CRC Press

Published: 2021-09-08

Total Pages: 220

ISBN-13: 100042166X

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Book Synopsis Machine Translation and Transliteration involving Related, Low-resource Languages by : Anoop Kunchukuttan

Download or read book Machine Translation and Transliteration involving Related, Low-resource Languages written by Anoop Kunchukuttan and published by CRC Press. This book was released on 2021-09-08 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Translation and Transliteration involving Related, Low-resource Languages discusses an important aspect of natural language processing that has received lesser attention: translation and transliteration involving related languages in a low-resource setting. This is a very relevant real-world scenario for people living in neighbouring states/provinces/countries who speak similar languages and need to communicate with each other, but training data to build supporting MT systems is limited. The book discusses different characteristics of related languages with rich examples and draws connections between two problems: translation for related languages and transliteration. It shows how linguistic similarities can be utilized to learn MT systems for related languages with limited data. It comprehensively discusses the use of subword-level models and multilinguality to utilize these linguistic similarities. The second part of the book explores methods for machine transliteration involving related languages based on multilingual and unsupervised approaches. Through extensive experiments over a wide variety of languages, the efficacy of these methods is established. Features Novel methods for machine translation and transliteration between related languages, supported with experiments on a wide variety of languages. An overview of past literature on machine translation for related languages. A case study about machine translation for related languages between 10 major languages from India, which is one of the most linguistically diverse country in the world. The book presents important concepts and methods for machine translation involving related languages. In general, it serves as a good reference to NLP for related languages. It is intended for students, researchers and professionals interested in Machine Translation, Translation Studies, Multilingual Computing Machine and Natural Language Processing. It can be used as reference reading for courses in NLP and machine translation. Anoop Kunchukuttan is a Senior Applied Researcher at Microsoft India. His research spans various areas on multilingual and low-resource NLP. Pushpak Bhattacharyya is a Professor at the Department of Computer Science, IIT Bombay. His research areas are Natural Language Processing, Machine Learning and AI (NLP-ML-AI). Prof. Bhattacharyya has published more than 350 research papers in various areas of NLP.

A Generic Character Aligned Machine Transliteration System for Indic Languages

Author: Nikhil Londhe

Publisher:

Published: 2013

Total Pages: 32

ISBN-13:

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A typical problem encountered in machine translation is the Out of Vocabulary (OOV) terms. These are usually names of places, people or technical terms that cannot be easily translated from one language to another or become obfuscated when translated. These end up as transliterated terms, i.e., a syllable or syllable group conversion from one language to another while trying to preserve the phonetic pronunciation. Although a large number of transliteration systems have been built over the years, they suffer from several problems. Firstly, any machine learning system is only as good as the underlying dataset used to train the system. For resource poor languages thus, either no such systems exist or perform extremely poorly. Secondly, most transliteration systems are over fitted to cater to the source language. However, with the proliferation of the Internet and the social media, language mixing is fairly common and most such systems fail if words derived from other languages are introduced. In this research, we aim to build better transliteration systems that can better model the language under consideration and incorporate additional features that can offset the over fitting problem described above. Also we explore how inherent language similarities can be used to bootstrap transliteration systems for resource poor languages. We explore how classical techniques in machine translation and information retrieval can be adapted to the problem in hand to build better and more robust systems.

Book Synopsis A Generic Character Aligned Machine Transliteration System for Indic Languages by : Nikhil Londhe

Download or read book A Generic Character Aligned Machine Transliteration System for Indic Languages written by Nikhil Londhe and published by . This book was released on 2013 with total page 32 pages. Available in PDF, EPUB and Kindle. Book excerpt: A typical problem encountered in machine translation is the Out of Vocabulary (OOV) terms. These are usually names of places, people or technical terms that cannot be easily translated from one language to another or become obfuscated when translated. These end up as transliterated terms, i.e., a syllable or syllable group conversion from one language to another while trying to preserve the phonetic pronunciation. Although a large number of transliteration systems have been built over the years, they suffer from several problems. Firstly, any machine learning system is only as good as the underlying dataset used to train the system. For resource poor languages thus, either no such systems exist or perform extremely poorly. Secondly, most transliteration systems are over fitted to cater to the source language. However, with the proliferation of the Internet and the social media, language mixing is fairly common and most such systems fail if words derived from other languages are introduced. In this research, we aim to build better transliteration systems that can better model the language under consideration and incorporate additional features that can offset the over fitting problem described above. Also we explore how inherent language similarities can be used to bootstrap transliteration systems for resource poor languages. We explore how classical techniques in machine translation and information retrieval can be adapted to the problem in hand to build better and more robust systems.

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.

Book Synopsis Challenges for Arabic Machine Translation by : Abdelhadi Soudi

Download or read book Challenges for Arabic Machine Translation written by Abdelhadi Soudi and published by John Benjamins Publishing. This book was released on 2012-08-01 with total page 167 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Hybrid Machine Translation for Low-Resource Languages

Author: George Joe

Publisher:

Published: 2023-11-02

Total Pages: 0

ISBN-13:

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The book "Hybrid Machine Translation for Low-Resource Languages" authored by George Joe provides a comprehensive overview of the development and evaluation of hybrid machine translation systems for English to Indian languages under low-resource conditions. The book discusses the challenges faced in developing machine translation systems for low-resource languages and how hybrid approaches can be used to overcome these challenges. The author presents a detailed analysis of various hybrid machine translation techniques such as rule-based, statistical, and neural machine translation, and how these techniques can be integrated to improve translation quality and efficiency. The book also covers the use of machine learning techniques such as transfer learning and active learning to improve the performance of machine translation systems. The book provides numerous case studies and practical examples of the development and evaluation of hybrid machine translation systems for low-resource languages. The author also discusses the importance of creating parallel corpora for low-resource languages and the challenges involved in creating such corpora. This book is a valuable resource for researchers and practitioners working in the field of natural language processing, machine learning, and machine translation. It provides a comprehensive understanding of the challenges involved in developing machine translation systems for low-resource languages and the ways in which hybrid approaches can be used to overcome these challenges. It also highlights the importance of creating parallel corpora for low-resource languages to improve the performance of machine translation systems.

Book Synopsis Hybrid Machine Translation for Low-Resource Languages by : George Joe

Download or read book Hybrid Machine Translation for Low-Resource Languages written by George Joe and published by . This book was released on 2023-11-02 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book "Hybrid Machine Translation for Low-Resource Languages" authored by George Joe provides a comprehensive overview of the development and evaluation of hybrid machine translation systems for English to Indian languages under low-resource conditions. The book discusses the challenges faced in developing machine translation systems for low-resource languages and how hybrid approaches can be used to overcome these challenges. The author presents a detailed analysis of various hybrid machine translation techniques such as rule-based, statistical, and neural machine translation, and how these techniques can be integrated to improve translation quality and efficiency. The book also covers the use of machine learning techniques such as transfer learning and active learning to improve the performance of machine translation systems. The book provides numerous case studies and practical examples of the development and evaluation of hybrid machine translation systems for low-resource languages. The author also discusses the importance of creating parallel corpora for low-resource languages and the challenges involved in creating such corpora. This book is a valuable resource for researchers and practitioners working in the field of natural language processing, machine learning, and machine translation. It provides a comprehensive understanding of the challenges involved in developing machine translation systems for low-resource languages and the ways in which hybrid approaches can be used to overcome these challenges. It also highlights the importance of creating parallel corpora for low-resource languages to improve the performance of machine translation systems.

Improving Neural Machine Translation for Low-resource Languages

Author: Toan Q. Nguyen

Publisher:

Published: 2021

Total Pages: 89

ISBN-13:

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Book Synopsis Improving Neural Machine Translation for Low-resource Languages by : Toan Q. Nguyen

Download or read book Improving Neural Machine Translation for Low-resource Languages written by Toan Q. Nguyen and published by . This book was released on 2021 with total page 89 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Addressing Issues of Learner Diversity in English Language Education

Author: Tran, Thao Quoc

Publisher: IGI Global

Published: 2024-04-22

Total Pages: 377

ISBN-13:

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In the dynamic context of English language education, learners bring many differences in identity, motivation, engagement, ability, and more. Addressing Issues of Learner Diversity in English Language Education recognizes that traditional, one-size-fits-all approaches to language education are insufficient in meeting the needs of a varied and global learner population. It grapples with effectively teaching English to individuals with diverse linguistic backgrounds, learning styles, and cultural contexts. The challenges range from learner autonomy and motivation issues to navigating mixed-level classes and integrating technology into language teaching. Drawing on current research trends and cutting-edge methodologies, this book captures the diverse voices of contributors from various ESL/EFL settings, offering context-specific solutions to the myriad challenges faced in language education. The book illuminates the nuanced phenomena within English language education; it showcases innovative theoretical frameworks and up-to-date research findings. By addressing learners as singular individuals and collectives, the publication guides educators in enhancing individual competencies and maximizing the potential of each learner.

Book Synopsis Addressing Issues of Learner Diversity in English Language Education by : Tran, Thao Quoc

Download or read book Addressing Issues of Learner Diversity in English Language Education written by Tran, Thao Quoc and published by IGI Global. This book was released on 2024-04-22 with total page 377 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the dynamic context of English language education, learners bring many differences in identity, motivation, engagement, ability, and more. Addressing Issues of Learner Diversity in English Language Education recognizes that traditional, one-size-fits-all approaches to language education are insufficient in meeting the needs of a varied and global learner population. It grapples with effectively teaching English to individuals with diverse linguistic backgrounds, learning styles, and cultural contexts. The challenges range from learner autonomy and motivation issues to navigating mixed-level classes and integrating technology into language teaching. Drawing on current research trends and cutting-edge methodologies, this book captures the diverse voices of contributors from various ESL/EFL settings, offering context-specific solutions to the myriad challenges faced in language education. The book illuminates the nuanced phenomena within English language education; it showcases innovative theoretical frameworks and up-to-date research findings. By addressing learners as singular individuals and collectives, the publication guides educators in enhancing individual competencies and maximizing the potential of each learner.

Machine Translation with Minimal Reliance on Parallel Resources

Author: George Tambouratzis

Publisher: Springer

Published: 2017-08-09

Total Pages: 92

ISBN-13: 3319631071

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This book provides a unified view on a new methodology for Machine Translation (MT). This methodology extracts information from widely available resources (extensive monolingual corpora) while only assuming the existence of a very limited parallel corpus, thus having a unique starting point to Statistical Machine Translation (SMT). In this book, a detailed presentation of the methodology principles and system architecture is followed by a series of experiments, where the proposed system is compared to other MT systems using a set of established metrics including BLEU, NIST, Meteor and TER. Additionally, a free-to-use code is available, that allows the creation of new MT systems. The volume is addressed to both language professionals and researchers. Prerequisites for the readers are very limited and include a basic understanding of the machine translation as well as of the basic tools of natural language processing.

Book Synopsis Machine Translation with Minimal Reliance on Parallel Resources by : George Tambouratzis

Download or read book Machine Translation with Minimal Reliance on Parallel Resources written by George Tambouratzis and published by Springer. This book was released on 2017-08-09 with total page 92 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a unified view on a new methodology for Machine Translation (MT). This methodology extracts information from widely available resources (extensive monolingual corpora) while only assuming the existence of a very limited parallel corpus, thus having a unique starting point to Statistical Machine Translation (SMT). In this book, a detailed presentation of the methodology principles and system architecture is followed by a series of experiments, where the proposed system is compared to other MT systems using a set of established metrics including BLEU, NIST, Meteor and TER. Additionally, a free-to-use code is available, that allows the creation of new MT systems. The volume is addressed to both language professionals and researchers. Prerequisites for the readers are very limited and include a basic understanding of the machine translation as well as of the basic tools of natural language processing.

Evaluation of a Machine Translation System for Low Resource Languages

Author: Vincent Vandeghinste

Publisher:

Published: 2008

Total Pages: 8

ISBN-13:

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Book Synopsis Evaluation of a Machine Translation System for Low Resource Languages by : Vincent Vandeghinste

Download or read book Evaluation of a Machine Translation System for Low Resource Languages written by Vincent Vandeghinste and published by . This book was released on 2008 with total page 8 pages. Available in PDF, EPUB and Kindle. Book excerpt: