  Language pairs with limited amounts of parallel data, also known as
low-resource languages, remain a challenge for neural machine translation.
While the Transformer model has achieved significant improvements for many
language pairs and has become the de facto mainstream architecture, its
capability under low-resource conditions has not been fully investigated yet.
Our experiments on different subsets of the IWSLT14 training data show that the
effectiveness of Transformer under low-resource conditions is highly dependent
on the hyper-parameter settings. Our experiments show that using an optimized
Transformer for low-resource conditions improves the translation quality up to
7.3 BLEU points compared to using the Transformer default settings.
