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Deep encoders have been proven to be effective in improving neural machinetranslation (NMT) systems, but it reaches the upper bound of translationquality when the number of encoder layers exceeds 18. Worse still, deepernetworks consume a lot of memory, making it impossible to train efficiently. Inthis paper, we present Symbiosis Networks, which include a full network as theSymbiosis Main Network (M-Net) and another shared sub-network with the samestructure but less layers as the Symbiotic Sub Network (S-Net). We adoptSymbiosis Networks on Transformer-deep (m-n) architecture and define aparticular regularization loss $\mathcal{L}_{\tau}$ between the M-Net and S-Netin NMT. We apply joint-training on the Symbiosis Networks and aim to improvethe M-Net performance. Our proposed training strategy improves Transformer-deep(12-6) by 0.61, 0.49 and 0.69 BLEU over the baselines under classic training onWMT'14 EN->DE, DE->EN and EN->FR tasks. Furthermore, our Transformer-deep(12-6) even outperforms classic Transformer-deep (18-6).

Joint-training on Symbiosis Networks for Deep Nueral Machine Translation models