Measuring the Impact of Data Augmentation Methods for Extremely Low-Resource NMT

Abstract

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.