Neural Machine Translation (NMT)
systems require a lot of data to be competitive. For this reason, data selection techniques are used only for finetuning systems that have been trained
with larger amounts of data. In this
work we aim to use Feature Decay Algorithms (FDA) data selection techniques not only to fine-tune a system but also to build a complete system with less data. Our findings reveal that it is possible to find a subset
of sentence pairs, that outperforms by
1.11 BLEU points the full training corpus, when used for training a GermanEnglish NMT system .