Empirical Analysis on Machine Translation Possibilities for Low-Resource Santali Language.

Low-resource Indian language development is a crucial and challenging task due to the linguistic diversity within India. This paper overviews the work and the importance of creating technologies and resources for these languages. India has about 19,500 languages, yet only 122 are considered significant. Many languages lack comprehensive linguistic resources, such as linguistic tools, well-annotated corpora, and natural language processing (NLP) models. These essential resources are missing for numerous languages, hindering the development of NLP systems. Of the approximately 1.4 billion people in India, only 20% are proficient in English, which is the dominant language of many online resources. Therefore, translating content into local languages is crucial for improving communication. For several reasons, it is crucial to develop technologies and resources for underrepresented Indian languages. This review focuses on (1) A comprehensive analysis of machine translation systems, (2) Key challenges in interface-equipped machine translation systems, (3) The application of automated machine translation, (4) Difficulties with automated machine translation, and (5) A thorough description of the methodologies and results related to machine translation. The paper analyzes several concerns and proposes efficient solutions to address these challenges. [ABSTRACT FROM AUTHOR]