  Transformers have achieved great success in machine translation, but
transformer-based NMT models often require millions of bilingual parallel
corpus for training. In this paper, we propose a novel architecture named as
attention link (AL) to help improve transformer models' performance, especially
in low training resources. We theoretically demonstrate the superiority of our
attention link architecture in low training resources. Besides, we have done a
large number of experiments, including en-de, de-en, en-fr, en-it, it-en, en-ro
translation tasks on the IWSLT14 dataset as well as real low resources scene on
bn-gu and gu-ta translation tasks on the CVIT PIB dataset. All the experiment
results show our attention link is powerful and can lead to a significant
improvement. In addition, we achieve a 37.9 BLEU score, a new sota, on the
IWSLT14 de-en task by combining our attention link and other advanced methods.
