  Recent studies have revealed some issues of Multi-Head Attention (MHA), e.g.,
redundancy and over-parameterization. Specifically, the heads of MHA were
originally designed to attend to information from different representation
subspaces, whereas prior studies found that some attention heads likely learn
similar features and can be pruned without harming performance. Inspired by the
minimum-redundancy feature selection, we assume that focusing on the most
representative and distinctive features with minimum resources can mitigate the
above issues and lead to more effective and efficient MHAs. In particular, we
propose Grouped Head Attention, trained with a self-supervised group constraint
that group attention heads, where each group focuses on an essential but
distinctive feature subset. We additionally propose a Voting-to-Stay procedure
to remove redundant heads, thus achieving a transformer with lighter weights.
Moreover, our method achieves significant performance gains on three
well-established tasks while considerably compressing parameters.
