In this paper, we explore alternative ways to train a neural machine
translation system in a multi-domain scenario. We investigate data
concatenation (with fine tuning), model stacking (multi-level fine tuning),
data selection and multi-model ensemble. Our findings show that the best
translation quality can be achieved by building an initial system on a
concatenation of available out-of-domain data and then fine-tuning it on
in-domain data. Model stacking works best when training begins with the
furthest out-of-domain data and the model is incrementally fine-tuned with the
next furthest domain and so on. Data selection did not give the best results,
but can be considered as a decent compromise between training time and
translation quality. A weighted ensemble of different individual models
performed better than data selection. It is beneficial in a scenario when there
is no time for fine-tuning an already trained model.

本文探索了在多领域场景下训练神经机器翻译系统的替代方法，结果表明最佳翻译质量可通过在可用领域数据的串联上构建初始系统并在领域内数据上进行微调来实现。