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

Sub-Word Alignment Is Still Useful: A Vest-Pocket Method for Enhancing Low-Resource Machine Translation