Impact Of Filtering Generated Pseudo Bilingual Texts In Low-Resource Neural Machine Translation Enhancement: The Case Of Persian-Spanish

Although the Neural Machine Translation (NMT) framework has already been shown effective in large training data scenarios, it is less effective for low-resource conditions. To improve NMT performance in a low-resource setting, we extend the high-quality training data by generating a pseudo bilingual dataset and then filtering out low-quality alignments using a quality estimation based on back-translation. We demonstrate that our approach yields significantly higher BLEU scores than those of a set of baselines. (C) 2021 The Authors. Published by Elsevier B.V.