Neural Machine Translation of Low-Resource and Similar Languages with Backtranslation

Abstract

We present our contribution to the WMT19 Similar Language Translation shared task. We investigate the utility of neural machine translation on three low-resource, similar language pairs: Spanish -- Portuguese, Czech -- Polish, and Hindi -- Nepali. Since state-of-the-art neural machine translation systems still require large amounts of bitext, which we do not have for the pairs we consider, we focus primarily on incorporating monolingual data into our models with backtranslation. In our analysis, we found Transformer models to work best on Spanish -- Portuguese and Czech -- Polish translation, whereas LSTMs with global attention worked best on Hindi -- Nepali translation.

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