  We leverage embedding duplication between aligned sub-words to extend the
Parent-Child transfer learning method, so as to improve low-resource machine
translation. We conduct experiments on benchmark datasets of My-En, Id-En and
Tr-En translation scenarios. The test results show that our method produces
substantial improvements, achieving the BLEU scores of 22.5, 28.0 and 18.1
respectively. In addition, the method is computationally efficient which
reduces the consumption of training time by 63.8%, reaching the duration of 1.6
hours when training on a Tesla 16GB P100 GPU. All the models and source codes
in the experiments will be made publicly available to support reproducible
research.
