Character-based Unsupervised Transliteration. Unsupervised Substring-based Transliteration. Utilizing Orthographic Similarity for Unsupervised Transliteration. Translation from English to Indian Languages. Lexical Similarity between Indian Languages. ![]() A Case Study on Indic Language Translation. Choice of Pivot Language and Language Relatedness. Investigation of Design Choices and Hyperparameters. Improving Subword-level Translation Quality. Why are Subword Units better than other Translation Units? Summary and Future Work. Training Subword-level Translation Models. Utilizing Lexical Similarity by using Subword Translation Units. Rule-based MT Systems involving Related Languages. Neural Machine Translation and Related Languages. Translation involving Related Languages and a Lingua franca. What does the monograph contain? Past Work on MT for Related Languages. Do we need SMT approaches customized for Related Languages? Translation, Transliteration and Related Languages: The Connection. Language Relatedness: Origins and Key Properties. Need for Machine Translation involving Related Languages. Need for Machine Translation and Transliteration. Bhattacharyya has published more than 350 research papers in various areas of NLP. His research areas are Natural Language Processing, Machine Learning and AI (NLP-ML-AI). ![]() Pushpak Bhattacharyya is a Professor at the Department of Computer Science, IIT Bombay. His research spans various areas on multilingual and low-resource NLP. It can be used as reference reading for courses in NLP and machine translation.Īnoop Kunchukuttan is a Senior Applied Researcher at Microsoft India. It is intended for students, researchers and professionals interested in Machine Translation, Translation Studies, Multilingual Computing Machine and Natural Language Processing. In general, it serves as a good reference to NLP for related languages. The book presents important concepts and methods for machine translation involving 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.An overview of past literature on machine translation for related languages.Novel methods for machine translation and transliteration between related languages, supported with experiments on a wide variety of languages.Through extensive experiments over a wide variety of languages, the efficacy of these methods is established. The second part of the book explores methods for machine transliteration involving related languages based on multilingual and unsupervised approaches. It comprehensively discusses the use of subword-level models and multilinguality to utilize these linguistic similarities. It shows how linguistic similarities can be utilized to learn MT systems for related languages with limited data. The book discusses different characteristics of related languages with rich examples and draws connections between two problems: translation for related languages and transliteration. 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. In Transcription, in template Polymerase moves over, whereas, in translation, Ribosome moves over the mRNA.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.In Transcription, RNA polymerases are required, whereas in translation different reagents are used to create a polypeptide chain. ![]() In Transcription, slicing is involved, whereas in translation slicing is absent.In Transcription, rRNA, tRNA, and mRNA are formed, and all these forms of RNA are used in the process of translation.four types of ribo-nucleoside triphosphates are used as raw material, whereas in translation 20 different types of amino acids are used as raw materials. Transcription happens inside the cytoplasm in prokaryotes and nucleus in eukaryotes, whereas translation happens in cytoplasm.In transcription, the antisense strand of the DNA is used as a template, whereas in translation mRNA acts as a template.Transcription is the formation of RNA from DNA, whereas translation is the synthesizing RNA to make proteins i.e.But, there exists an essential difference between transcription and translation, such as
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