The Usefulness of Bibles in Low-Resource Machine Translation

Abstract

Bibles are available in a wide range of languages, which provides valuable parallel text between languages since verses can be aligned accurately between all the different translations. How well can such data be utilized to train good neural machine translation (NMT) models? We are particularly interested in low-resource languages of high morphological complexity, and attempt to answer this question in the current work by training and evaluating Basque-English and Navajo-English MT models with the Transformer architecture. Different tokenization methods are applied, among which syllabification turns out to be most effective for Navajo and it is also good for Basque. Another additional data resource which can be potentially available for endangered languages is a dictionary of either word or phrase translations, thanks to linguists’ work on language documentation. Could this data be leveraged to augment Bible data for better performance? We experiment with different ways to utilize dictionary data, and find that word-to-word mapping translation with a word-pair dictionary is more effective than low-resource techniques such as backtranslation or adding dictionary data directly into the training set, though neither backtranslation nor word-to-word mapping translation produce improvements over using Bible data alone in our experiments.