Transfer Learning for Low-Resource Neural Machine Translation

TL;DR

This paper introduces a transfer learning approach for neural machine translation that significantly enhances translation quality for low-resource languages by leveraging high-resource language models.

Contribution

The authors propose a transfer learning method that initializes low-resource NMT models with parameters from high-resource models, improving BLEU scores substantially.

Findings

Improved BLEU scores by an average of 5.6 on four low-resource language pairs.

Ensembling and unknown word replacement further increased BLEU by 2.

Transfer learning models can re-score and enhance SBMT systems, surpassing previous performance.

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.