  It is well known that translations of songs and poems not only break rhythm
and rhyming patterns, but can also result in loss of semantic information. The
Bhagavad Gita is an ancient Hindu philosophical text originally written in
Sanskrit that features a conversation between Lord Krishna and Arjuna prior to
the Mahabharata war. The Bhagavad Gita is also one of the key sacred texts in
Hinduism and is known as the forefront of the Vedic corpus of Hinduism. In the
last two centuries, there has been a lot of interest in Hindu philosophy from
western scholars; hence, the Bhagavad Gita has been translated in a number of
languages. However, there is not much work that validates the quality of the
English translations. Recent progress of language models powered by deep
learning has enabled not only translations but a better understanding of
language and texts with semantic and sentiment analysis. Our work is motivated
by the recent progress of language models powered by deep learning methods. In
this paper, we present a framework that compares selected translations (from
Sanskrit to English) of the Bhagavad Gita using semantic and sentiment
analyses. We use hand-labelled sentiment dataset for tuning state-of-art deep
learning-based language model known as bidirectional encoder representations
from transformers (BERT). We provide sentiment and semantic analysis for
selected chapters and verses across translations. Our results show that
although the style and vocabulary in the respective translations vary widely,
the sentiment analysis and semantic similarity shows that the message conveyed
are mostly similar.
