Hybridization Based Machine Translations for Low-Resource Language with Language Divergence

Abstract

A hybridised form of direct and rule-based language processing is used in this paper to present a Machine translation system from Sanskrit to Hindi. The divergence between Sanskrit and Hindi is also discussed in this paper, along with a proposition for how to handle it. Sanskrit-Hindi bilingual dictionaries, Grammatical Sanskrit corpus and a Sanskrit analyses rule base, have all been used in the projected system. The projected system's ability to access data from various data vocabularies and rule bases utilised in the system expansion has been improved by the usage of Elasticsearch technique. Additionally, a novel technique that builds a parse tree from the parsing table is presented in this paper. The system processes the input Sanskrit sentence using the parsing approach and the Context Free Grammar in normal form for Sanskrit language processing. No standard Sanskrit-Hindi Grammatical corpora available for Machine Translation which is designed and developed in the proposed work. The specific language sentence is produced using the Grammatical corpora and bilingual dictionaries. The proposed system achieved a Bilingual Evaluation Understudy (BLEU) score of 51.6 percent after being tested using Python's natural language toolkit API. The proposed system performs better than current systems when compared to cutting-edge systems, according to the comparison.