Meta-Learning for Few-Shot NMT Adaptation

Amr Sharaf; Hany Hassan; Hal Daumé III

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arXiv · 2004.02745

Meta-Learning for Few-Shot NMT Adaptation

Amr Sharaf·Hany Hassan·Hal Daumé III

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Abstract

We present META-MT, a meta-learning approach to adapt Neural Machine Translation (NMT) systems in a few-shot setting. META-MT provides a new approach to make NMT models easily adaptable to many target domains with the minimal amount of in-domain data. We frame the adaptation of NMT systems as a meta-learning problem, where we learn to adapt to new unseen domains based on simulated offline meta-training domain adaptation tasks. We evaluate the proposed meta-learning strategy on ten domains with general large scale NMT systems. We show that META-MT significantly outperforms classical domain adaptation when very few in-domain examples are available. Our experiments shows that META-MT can outperform classical fine-tuning by up to 2.5 BLEU points after seeing only 4, 000 translated words (300 parallel sentences).

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Amr Sharaf, Hany Hassan, Hal Daumé III. 2020-04-06. Meta-Learning for Few-Shot NMT Adaptation. https://arxiv.org/abs/2004.02745

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