  In this work we investigate the impact of applying textual data augmentation
tasks to low resource machine translation. There has been recent interest in
investigating approaches for training systems for languages with limited
resources and one popular approach is the use of data augmentation techniques.
Data augmentation aims to increase the quantity of data that is available to
train the system. In machine translation, majority of the language pairs around
the world are considered low resource because they have little parallel data
available and the quality of neural machine translation (NMT) systems depend a
lot on the availability of sizable parallel corpora. We study and apply three
simple data augmentation techniques popularly used in text classification
tasks; synonym replacement, random insertion and contextual data augmentation
and compare their performance with baseline neural machine translation for
English-Swahili (En-Sw) datasets. We also present results in BLEU, ChrF and
Meteor scores. Overall, the contextual data augmentation technique shows some
improvements both in the $EN \rightarrow SW$ and $SW \rightarrow EN$
directions. We see that there is potential to use these methods in neural
machine translation when more extensive experiments are done with diverse
datasets.
