Scalable Transformers for Neural Machine Translation

Transformer has been widely adopted in Neural Machine Translation (NMT) because of its large capacity and parallel training of sequence generation. However, the deployment of Transformer is challenging because different use scenarios require models of different complexities and scales. Naively training multiple Transformers of different scales is redundant in terms of both computation and memory. In this paper, we propose a novel scalable Transformers, which naturally contains sub-Transformers of different scales and have shared parameters. Each sub-Transformer can be easily obtained by cropping the parameters of the largest Transformer. A three-stage training scheme is proposed to tackle the difficulty of training the scalable Transformers, which introduces additional supervisions from word-level and sequence-level self-distillation. Extensive experiments were conducted on WMT EN-De and En-Fr to validate the effectiveness of the proposed scalable Transformers.