Machine Learning Classification of Biblical Translations Across Languages and Literary Genres

Abstract

For many the world over, the Bible is a foundational source of authority, one that is vital to understand. Yet the Bible is also the most translated text in history, and decisions made by translators are hugely impactful on our understanding of what we read. An important factor that goes into the translation of any text is its genre; Bible translations must take into consideration Biblical genre. While there are many ways to evaluate translation styles and efforts have been made to provide translators with resources to translate accurately, consistency across translations within Biblical genres is one that has not been deeply studied. We aim to make an initial contribution to this area of research by approaching Biblical genre from a quantitative angle. By training and testing logistic regression, multiclass regression with catboost, and random forest models on 33 different translations of the Bible in 4 different languages, we will understand not only which model is best suited to the task of classifying verses based on Biblical genre, but we will also determine whether differences in Biblical genre are distinctive enough to be quantifiably recognizable. This research will allow us to set a foundation for future research on the impact of translation methodology and language of translation on understanding Biblical genre.