Machine Translation and Transliteration involving Related, Low-resource Languages

Machine Translation and Transliteration involving Related, Low-resource Languages

涉及相关低资源语言的机器翻译与音译
计算机科学技术基础学科

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作者

Kunchukuttan AnoopBhattacharyya Pushpak

出版社

Chapman and Hall/CRC

出版时间

2021年09月08日

装帧

精装

ＩＳＢＮ

9780367561994

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页码

200

开本

0 x 0 x 0 cm

语种

英文

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暂无评分

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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 C

Preface. Introduction. Need for Machine Translation and Transliteration. Need for Machine Translation involving Related Languages. Language Relatedness: Origins and Key Properties. Do we need SMT approaches customized for Related Languages? Translation, Transliteration and Related Languages: The Connection. What does the monograph contain? Past Work on MT for Related Languages. Translation between Related Languages. Translation involving Related Languages and a Lingua franca. Neural Machine Translation and Related Languages. Rule-based MT Systems involving Related Languages. Summary. I Machine Translation. Utilizing Lexical Similarity by using Subword Translation Units. Motivation. Related Work. Translation Units for Related Languages. Training Subword-level Translation Models. Experimental Setup. Results and Discussion. Why are Subword Units better than other Translation Units? Summary and Future Work. Improving Subword-level Translation Quality. Effect of Resource Availability. Investigation of Design Choices and Hyperparameters. Improving Decoding Speed. Summary. Subword-level Pivot-based SMT. Motivation. Pivotbased SMT for Related Languages. Related Work. Experimental Setup. Results and Analysis. Using Multiple, Related Pivot Languages. Choice of Pivot Language and Language Relatedness. Summary and Future Directions. A Case Study on Indic Language Translation. A Primer on Indian Languages. Relatedness among Indian Languages. Dataset used for Study. Lexical Similarity between Indian Languages. Translation between Indian Languages. Translation from English to Indian Languages. II Machine Transliteration. Utilizing Orthographic Similarity for Unsupervised Transliteration. Motivation. Related Work. Unsupervised Substring-based Transliteration. Character-based Unsupervised Transliteration. Bootstrapping Substring-based models. Experimental Setup. Results and Discussion. Summary and Future Work. Multilingual Neural Transliteration. Motivation. Related Work. Multilingual Transliteration Learning. Experimental Setup. Results and Discussion. Zeroshot Transliteration. Incorporating Phonetic Information. Summary and Future Work. Conclusion and Future Directions. Conclusions. Future Work and Directions. Appendices. A Extended ITRANS Romanization Scheme. B Software and Data Resources. C Conferences/Workshops for Translation between Related Languages. Bibliography.

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