Machine Translation and Transliteration Involving Related and Low-resource Languages - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Machine Translation and Transliteration Involving Related and Low-resource Languages write by Anoop Kunchukuttan. This book was released on 2021-08-12. Machine Translation and Transliteration Involving Related and Low-resource Languages available in PDF, EPUB and Kindle. 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
Machine Translation and Transliteration involving Related, Low-resource Languages - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Machine Translation and Transliteration involving Related, Low-resource Languages write by Anoop Kunchukuttan. This book was released on 2021-09-08. Machine Translation and Transliteration involving Related, Low-resource Languages available in PDF, EPUB and Kindle. 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
Machine Translation and Transliteration involving Related, Low-resource Languages - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Machine Translation and Transliteration involving Related, Low-resource Languages write by Anoop Kunchukuttan. This book was released on 2021-08-12. Machine Translation and Transliteration involving Related, Low-resource Languages available in PDF, EPUB and Kindle. 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
A Generic Character Aligned Machine Transliteration System for Indic Languages - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook A Generic Character Aligned Machine Transliteration System for Indic Languages write by Nikhil Londhe. This book was released on 2013. A Generic Character Aligned Machine Transliteration System for Indic Languages available in PDF, EPUB and Kindle. 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
Challenges for Arabic Machine Translation - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Challenges for Arabic Machine Translation write by Abdelhadi Soudi. This book was released on 2012-08-01. Challenges for Arabic Machine Translation available in PDF, EPUB and Kindle. 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.
