  The powerful modeling capabilities of all-attention-based transformer
architectures often cause overfitting and - for natural language processing
tasks - lead to an implicitly learned internal language model in the
autoregressive transformer decoder complicating the integration of external
language models. In this paper, we explore relaxed attention, a simple and
easy-to-implement smoothing of the attention weights, yielding a two-fold
improvement to the general transformer architecture: First, relaxed attention
provides regularization when applied to the self-attention layers in the
encoder. Second, we show that it naturally supports the integration of an
external language model as it suppresses the implicitly learned internal
language model by relaxing the cross attention in the decoder. We demonstrate
the benefit of relaxed attention across several tasks with clear improvement in
combination with recent benchmark approaches. Specifically, we exceed the
former state-of-the-art performance of 26.90% word error rate on the largest
public lip-reading LRS3 benchmark with a word error rate of 26.31%, as well as
we achieve a top-performing BLEU score of 37.67 on the IWSLT14
(DE$\rightarrow$EN) machine translation task without external language models
and virtually no additional model parameters. Code and models will be made
publicly available.
