Machine Translation and Transliteration involving Related, Low-resource Languages
Chapman & Hall/CRC (Verlag)
978-0-367-56200-7 (ISBN)
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.
Dr. Anoop Kunchukuttan is a Senior Applied Researcher in the machine translation team at Microsoft India, Hyderabad. He received his Ph.D from the Indian Institute of Technology Bombay. He is broadly interested in natural language processing and machine learning. His research interests include multilingual learning, language relatedness, machine translation, machine transliteration and distributional semantics. He has also explored problems in information extraction, automated grammar correction, multiword expressions and crowdsourcing for NLP. These works have been published in top-tier Natural Language Processing (NLP) conferences and journals. He is passionate about building software and resources for NLP in Indian languages. He actively develops and maintains the Indic NLP Library and the Indic NLP Catalog, and has contributed to the development of resources like the AI4Bharat Indic NLP Suite and the IIT Bombay parallel corpus. He is a co-organizer of the Workshop on Asian Translation and a co-founder of the AI4Bharat NLP Initiative. Dr. Pushpak Bhattacharyya is Professor of Computer Science and Engineering Department 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. His textbook ‘Machine Translation’ sheds light on all paradigms of machine translation with abundant examples from Indian Languages. Two recent monographs co-authored by him called 'Investigations in Computational Sarcasm' and 'Cognitively Inspired Natural Language Processing- An Investigation Based on Eye Tracking' describe cutting edge research in NLP and ML. Prof. Bhattacharyya is Fellow of Indian National Academy of Engineering (FNAE) and Abdul Kalam National Fellow. For sustained contribution to technology he received the Manthan Award of the Ministry of IT, P.K. Patwardhan Award of IIT Bombay and VNMM Award of IIT Roorkey. He is also a Distinguished Alumnus of IIT Kharagpur and past President of Association of Computational Linguistics.
Preface. Introduction. Past Work on MT for Related Languages. I Machine Translation. Utilizing Lexical Similarity by using Subword Translation Units. Improving Subword-level. Translation Quality. Subword-level Pivot-based SMT. A Case Study on Indic Language Translation. II Machine Transliteration. Utilizing Orthographic Similarity for Unsupervised Transliteration. Multilingual Neural Transliteration. Conclusion and Future Directions. Appendices. A Extended ITRANS Romanization Scheme. B Software and Data Resources. C Conferences/Workshops for Translation between Related Languages. Bibliography.
| Erscheinungsdatum | 05.09.2024 |
|---|---|
| Zusatzinfo | 56 Tables, black and white; 15 Line drawings, black and white; 2 Halftones, black and white; 17 Illustrations, black and white |
| Sprache | englisch |
| Maße | 156 x 234 mm |
| Gewicht | 408 g |
| Themenwelt | Mathematik / Informatik ► Informatik ► Theorie / Studium |
| ISBN-10 | 0-367-56200-6 / 0367562006 |
| ISBN-13 | 978-0-367-56200-7 / 9780367562007 |
| Zustand | Neuware |
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