A Pragmatic Analysis of Machine Translation Techniques for Preserving the Authenticity of the Sanskrit Language

Abstract

Machine Translation has been a field of study for over six decades, but it has acquired substantial prominence in the last decade as processing capacity in personal computers has increased. The purpose of this paper is to discuss the usage of Sanskrit as a source, target, or supporting language in various Machine Translation systems. To investigate Machine Translation, researchers use a variety of strategies, including corpus-based, direct, and rule-based approaches. The primary goal of employing Sanskrit in Machine Translation is to evaluate its appropriateness, lexicon, and performance when proper Machine Translation methods are used. The research examines various modelling strategies for developing a machine translation system, specifically Statistical and Neural Machine Translation, in order to bridge the gap between Sanskrit and its current successor, Hindi. Interpretations are formed in Statistical Machine Translation by matching words from the source and target languages with statistical models and bilingual text corpora to learn parameters. Neural Machine Translation, on the other hand, uses an artificial neural network to predict the likelihood of a word sequence, frequently modelling entire phrases within a single integrated model. Neural Machine Translation is implemented using an encoder-decoder architecture with an attention mechanism. One of the most significant contributions of this paper is the use of different data sources, data collecting, and scraping to create a complete dataset. According to the study's findings, Neural Machine Translation outperforms the Statistical Machine Translation modelling technique. Furthermore, the paper examines the distinctive qualities of the Sanskrit language as well as the difficulties encountered by researchers in digesting Sanskrit while constructing the machine translation system. This study investigates the use of Sanskrit in Machine Translation and analyses several modelling methods, such as Statistical and Neural Machine Translation. The paper emphasizes the advantages of Neural Machine Translation and discusses the unique characteristics and challenges of the Sanskrit language in machine translation development.