  This paper provides norm-based generalization bounds for the Transformer
architecture that do not depend on the input sequence length. We employ a
covering number based approach to prove our bounds. We use three novel covering
number bounds for the function class of bounded linear transformations to upper
bound the Rademacher complexity of the Transformer. Furthermore, we show this
generalization bound applies to the common Transformer training technique of
masking and then predicting the masked word. We also run a simulated study on a
sparse majority data set that empirically validates our theoretical findings.
