论文标题
元学习，用于几次NMT改编
Meta-Learning for Few-Shot NMT Adaptation
论文作者
论文摘要
我们提出了Meta-MT，这是一种在几次设置中适应神经机器翻译（NMT）系统的元学习方法。 Meta-MT提供了一种新的方法，使NMT模型易于适应许多目标域，并具有最小的域内数据。我们将NMT系统的改编作为一个元学习问题，根据模拟的离线元训练域的适应任务，我们学会了适应新的看不见的域。我们评估了具有一般大型NMT系统的十个领域的拟议元学习策略。我们表明，当很少有域内示例可用时，元MT明显优于经典域的适应性。我们的实验表明，在仅看到4,000个翻译单词（300个平行句子）之后，元MT的表现可以胜过高达2.5个BLEU点。
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).
