  Recent progress in neural machine translation is directed towards larger
neural networks trained on an increasing amount of hardware resources. As a
result, NMT models are costly to train, both financially, due to the
electricity and hardware cost, and environmentally, due to the carbon
footprint. It is especially true in transfer learning for its additional cost
of training the "parent" model before transferring knowledge and training the
desired "child" model. In this paper, we propose a simple method of re-using an
already trained model for different language pairs where there is no need for
modifications in model architecture. Our approach does not need a separate
parent model for each investigated language pair, as it is typical in NMT
transfer learning. To show the applicability of our method, we recycle a
Transformer model trained by different researchers and use it to seed models
for different language pairs. We achieve better translation quality and shorter
convergence times than when training from random initialization.
