Handling Rare Word Problem using Synthetic Training Data for Sinhala and Tamil Neural Machine Translation

Abstract

Lack of parallel training data influences the rare word problem in Neural Machine Translation (NMT) systems, particularly for under-resourced languages. Using synthetic parallel training data (data augmentation) is a promising approach to handle the rare word problem. Previously proposed methods for data augmentation do not consider language syntax when generating synthetic training data. This leads to generation of sentences that lower the overall quality of parallel training data. In this paper, we discuss the suitability of using Parts of Speech (POS) tagging and morphological analysis as syntactic features to prune the generated synthetic sentence pairs that do not adhere to language syntax. Our models show an overall 2.16 and 5.00 BLEU score gains over our benchmark Sinhala to Tamil and Tamil to Sinhala translation systems, respectively. Although we focus on Sinhala and Tamil NMT for the domain of official government documents, we believe that these synthetic data pruning techniques can be generalized to any language pair.

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