  Machine translation (MT) systems, especially when designed for an industrial
setting, are trained with general parallel data derived from the Web. Thus,
their style is typically driven by word/structure distribution coming from the
average of many domains. In contrast, MT customers want translations to be
specialized to their domain, for which they are typically able to provide text
samples. We describe an approach for customizing MT systems on specific domains
by selecting data similar to the target customer data to train neural
translation models. We build document classifiers using monolingual target
data, e.g., provided by the customers to select parallel training data from Web
crawled data. Finally, we train MT models on our automatically selected data,
obtaining a system specialized to the target domain. We tested our approach on
the benchmark from WMT-18 Translation Task for News domains enabling
comparisons with state-of-the-art MT systems. The results show that our models
outperform the top systems while using less data and smaller models.
