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.

我们提出了一种基于可解释性的 NMT 训练方法，应用于无监督和有监督模型训练，用于翻译英语和三种不同资源的语言 —— 法语、古吉拉特语和哈萨克语。我们的结果表明，我们的方法在低资源条件下可以有希望，优于简单的训练基线；尽管改进只是微小的，但为进一步探索这种方法和参数以及其扩展到其他语言奠定了基础。