Using data from the Bible, a team from the Dartmouth College, New Hampshire in the US, developed an algorithm trained on various versions of the sacred texts that can convert written works into different styles for different audiences.
The team saw in the Bible "a large, previously untapped dataset of aligned parallel text."
"The English-language Bible comes in many different written styles, making it the perfect source text to work with for style translation," said lead author Keith Carlson, a doctoral student at Dartmouth.
According to the study, published in the journal Royal Society Open Science, this is not the first parallel dataset created for style translation. But it is the first that uses the Bible.
Beyond providing infinite inspiration, each version of the Bible contains more than 31,000 verses that the researchers used to produce over 1.5 million unique pairings of source and target verses for machine-learning training sets.
It is already thoroughly indexed by the consistent use of book, chapter and verse numbers. The predictable organisation of the text across versions eliminates the risk of alignment errors that could be caused by automatic methods of matching different versions of the same text, the researchers noted.
"The Bible is a 'divine' data set to work with to study this task," said Daniel Rockmore, Professor of computer science at Dartmouth.
The team used 34 stylistically distinct Bible versions ranging in linguistic complexity from the "King James Version" to the "Bible in basic english."
The texts were fed into two algorithms -- a statistical machine translation system called "Moses" and a neural network framework commonly used in machine translation, "Seq2Seq."
Other texts that have been used in the past, ranging from Shakespeare to Wikipedia entries, provide data sets that are either much smaller or not as well suited for the task of learning style translation.
"Humans have been performing the task of organizing Bible texts for centuries, so we didn't have to put our faith into less reliable alignment algorithms," Rockmore said.
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