  With the advent of the Transformer architecture, Neural Machine Translation
(NMT) results have shown great improvement lately. However, results in
low-resource conditions still lag behind in both bilingual and multilingual
setups, due to the limited amount of available monolingual and/or parallel
data; hence, the need for methods addressing data scarcity in an efficient, and
explainable way, is eminent. We propose an explainability-based training
approach for NMT, applied in Unsupervised and Supervised model training, for
translation of three languages of varying resources, French, Gujarati, Kazakh,
to and from English. Our results show our method can be promising, particularly
when training in low-resource conditions, outperforming simple training
baselines; though the improvement is marginal, it sets the ground for further
exploration of the approach and the parameters, and its extension to other
languages.
