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Transfer learning has been proven as an effective technique for neural machine translation under low-resource conditions. Existing methods require a common target language, language relatedness, or specific training tricks and regimes. We present a simple transfer learning method, where we first train a "parent" model for a high-resource language pair and then continue the training on a lowresource pair only by replacing the training corpus. This "child" model performs significantly better thandoi:10.18653/v1/w18-6325 dblp:conf/wmt/KocmiB18 fatcat:htdtqruozfddlo7dqrsypzcfqy