Measuring the Impact of Data Augmentation Methods for Extremely Low-Resource NMT
Annie K. Lamar, Zeyneb Kaya · 2023
Data augmentation (DA) is a popular strategy to boost performance on neural machine translation tasks.The impact of data augmentation in low-resource environments, particularly for diverse and scarce languages, is understudied.In this paper, we introduce a simple yet novel metric to measure the impact of several different data augmentation strategies.This metric, which we call Data Augmentation Advantage (DAA), quantifies how many true data pairs a synthetic data pair is worth in a particular experimental context.We demonstrate the utility of this metric by training models for several linguisticallyvaried datasets using the data augmentation methods of back-translation, SwitchOut, and sentence concatenation.In lowerresource tasks, DAA is an especially valuable metric for comparing DA performance as it provides a more effective way to quantify gains when BLEU scores are especially small and results across diverse languages are more divergent and difficult to assess.