Textual Augmentation Techniques Applied to Low Resource Machine Translation: Case of Swahili
This work addresses the problem of limited parallel data for low-resource language pairs like English-Swahili, but it is incremental as it adapts existing methods from text classification to machine translation.
The study applied three textual data augmentation techniques to low-resource English-Swahili machine translation, finding that contextual data augmentation showed some improvements in BLEU, ChrF, and Meteor scores in both translation directions.
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.