  We propose a parameter sharing method for Transformers (Vaswani et al.,
2017). The proposed approach relaxes a widely used technique, which shares
parameters for one layer with all layers such as Universal Transformers
(Dehghani et al., 2019), to increase the efficiency in the computational time.
We propose three strategies: Sequence, Cycle, and Cycle (rev) to assign
parameters to each layer. Experimental results show that the proposed
strategies are efficient in the parameter size and computational time.
Moreover, we indicate that the proposed strategies are also effective in the
configuration where we use many training data such as the recent WMT
competition.
