Transfer Learning for Low-Resource Neural Machine Translation

Barret Zoph; Deniz Yuret; Jonathan May; Kevin Knight

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arXiv · 1604.02201

Transfer Learning for Low-Resource Neural Machine Translation

Barret Zoph·Deniz Yuret·Jonathan May·Kevin Knight

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Abstract

The encoder-decoder framework for neural machine translation (NMT) has been shown effective in large data scenarios, but is much less effective for low-resource languages. We present a transfer learning method that significantly improves Bleu scores across a range of low-resource languages. Our key idea is to first train a high-resource language pair (the parent model), then transfer some of the learned parameters to the low-resource pair (the child model) to initialize and constrain training. Using our transfer learning method we improve baseline NMT models by an average of 5.6 Bleu on four low-resource language pairs. Ensembling and unknown word replacement add another 2 Bleu which brings the NMT performance on low-resource machine translation close to a strong syntax based machine translation (SBMT) system, exceeding its performance on one language pair. Additionally, using the transfer learning model for re-scoring, we can improve the SBMT system by an average of 1.3 Bleu, improving the state-of-the-art on low-resource machine translation.

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Barret Zoph, Deniz Yuret, Jonathan May, Kevin Knight. 2016-04-08. Transfer Learning for Low-Resource Neural Machine Translation. https://arxiv.org/abs/1604.02201

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