Machine learning and other artificial intelligence (AI) methods have had immense success with scientific and technical tasks such as predicting how protein molecules fold and recognising faces in a crowd. However, the application of these methods to the humanities are yet to be fully explored.
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Copyright: theconversation.com – “AI, philosophy and religion: what machine learning can tell us about the Bhagavad Gita”
analyse English translations of the Bhagavad Gita, an ancient Hindu text written originally in Sanskrit.
Using a deep learning-based language model called BERT, we studied sentiment (emotions) and semantics (meanings) in the translations. Despite huge variations in vocabulary and sentence structure, we found that the patterns of emotion and meaning were broadly similar in all three.
This research opens a path to the use of AI-based technologies for comparing translations and reviewing sentiments in a wide range of texts.
An ancient book of wisdom
The Bhagavad Gita is one of the central Hindu sacred and philosophical texts. Written more than 2,000 years ago, it has been translated into more than 100 languages and has been of interest to western philosophers since the 18th century.
The text of the Bhagavad Gita relates a conversation between the Hindu deity Lord Krishna and a prince called Arjuna. They discuss whether a soldier should go to war for ethics and duty (or “dharma”) if they have close friends or family on the opposing side.
The text has been instrumental in laying the foundations of Hinduism. Among many other things, it is where the philosophy of karma (a spiritual principle of cause and effect) originates.
There have been countless English translations of the Bhagavad Gita, but there is not much work that validates their quality. Translations of songs and poems not only break rhythm and rhyming patterns, but can also result in the loss of semantic information.
In our research, we used deep learning language models to analyse three selected translations of the Bhagavad Gita (from Sanskrit to English) with semantic and sentiment analyses which help in the evaluation of translation quality.[…]
Read more: www.theconversation.com
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