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

• URL: http://arxiv.org/abs/2004.02071v1
• Date: Sun, 5 Apr 2020 02:14:14 GMT
• Title: Incorporating Bilingual Dictionaries for Low Resource Semi-Supervised Neural Machine Translation
• Authors: Sreyashi Nag and Mihir Kale and Varun Lakshminarasimhan and Swapnil Singhavi
• Abstract summary: 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.
• Score: 5.958653653305609
• License: http://creativecommons.org/licenses/by/4.0/
• 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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