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From the earliest of civilizations, language has served as the defining feature of people’s activities in regards to interaction with others and preservation of culture. Knowledge transmission and retention became easier as a result of language and its development. In today’s world, the need to tackle linguistic gaps has skyrocketed. English is an example of a language that has become a global lingua franca that enables people from different cultures to interact seamlessly while Sanskrit is an example of a language that has preserved ancient India’s intellectual and spiritual wealth. The focus and goal of this paper is to identify the hindrances faced when trying to translate these two languages and incorporate the latest advancements in machine learning, more specifically, Long Short Term Memory (LSTM) networks, to create a working system for English to Sanskrit translation.

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A Neural Network Approach for Translating English to Sanskrit Using LSTM Encoder-Decoder Architecture

  • 会议论文

  • 发表日期：

    2026年

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  • Neha Vaswani,
  • Krupa Mehta

摘要

From the earliest of civilizations, language has served as the defining feature of people’s activities in regards to interaction with others and preservation of culture. Knowledge transmission and retention became easier as a result of language and its development. In today’s world, the need to tackle linguistic gaps has skyrocketed. English is an example of a language that has become a global lingua franca that enables people from different cultures to interact seamlessly while Sanskrit is an example of a language that has preserved ancient India’s intellectual and spiritual wealth. The focus and goal of this paper is to identify the hindrances faced when trying to translate these two languages and incorporate the latest advancements in machine learning, more specifically, Long Short Term Memory (LSTM) networks, to create a working system for English to Sanskrit translation.