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arXiv · 2004.02071

Incorporating Bilingual Dictionaries for Low Resource Semi-Supervised Neural Machine Translation

Sreyashi Nag·Mihir Kale·Varun Lakshminarasimhan·Swapnil Singhavi
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Abstract

We explore ways of incorporating bilingual dictionaries to enable semi-supervised neural machine translation. Conventional back-translation methods have shown success in leveraging target side monolingual data. However, since the quality of back-translation models is tied to the size of the available parallel corpora, this could adversely impact the synthetically generated sentences in a low resource setting. We propose a simple data augmentation technique to address both this shortcoming. We incorporate widely available bilingual dictionaries that yield word-by-word translations to generate synthetic sentences. This automatically expands the vocabulary of the model while maintaining high quality content. Our method shows an appreciable improvement in performance over strong baselines.

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Sreyashi Nag, Mihir Kale, Varun Lakshminarasimhan, Swapnil Singhavi. 2020-04-05. Incorporating Bilingual Dictionaries for Low Resource Semi-Supervised Neural Machine Translation. https://arxiv.org/abs/2004.02071

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