  In this paper, we show that the combination of Phrase Pair Injection and
Corpus Filtering boosts the performance of Neural Machine Translation (NMT)
systems. We extract parallel phrases and sentences from the pseudo-parallel
corpus and augment it with the parallel corpus to train the NMT models. With
the proposed approach, we observe an improvement in the Machine Translation
(MT) system for 3 low-resource language pairs, Hindi-Marathi, English-Marathi,
and English-Pashto, and 6 translation directions by up to 2.7 BLEU points, on
the FLORES test data. These BLEU score improvements are over the models trained
using the whole pseudo-parallel corpus augmented with the parallel corpus.
