Domain Adaptation (DA) of Neural Machine Translation (NMT) model often relies
on a pre-trained general NMT model which is adapted to the new domain on a
sample of in-domain parallel data. Without parallel data, there is no way to
estimate the potential benefit of DA, nor the amount of parallel samples it
would require. It is however a desirable functionality that could help MT
practitioners to make an informed decision before investing resources in
dataset creation. We propose a Domain adaptation Learning Curve prediction
(DaLC) model that predicts prospective DA performance based on in-domain
monolingual samples in the source language. Our model relies on the NMT encoder
representations combined with various instance and corpus-level features. We
demonstrate that instance-level is better able to distinguish between different
domains compared to corpus-level frameworks proposed in previous studies.
Finally, we perform in-depth analyses of the results highlighting the
limitations of our approach, and provide directions for future research.

本文提出了一种基于源语种单语数据的领域适应学习曲线预测模型，该模型利用 NMT 编码器表示与各种实例和语料库级别特征相结合，能更好地区分不同领域，并探讨了该方法的局限性和未来研究方向。