On the Development of Customized Neural Machine Translation Models

Recent advances in neural modeling boosted performance of many machine learning applications. Training neural networks requires large amounts of clean data, which are rarely available; many methods have been designed and investigated by researchers to tackle this issue. As a partner of a project, we were asked to build translation engines for the weather forecast domain, relying on few, noisy data. Step by step, we developed neural translation models, which outperform by far Google Translate. This paper details our approach, that - we think - is paradigmatic for a broader category of applications of machine learning, and as such could be of widespread utility.