  In this paper, we introduce DropHead, a structured dropout method
specifically designed for regularizing the multi-head attention mechanism,
which is a key component of transformer, a state-of-the-art model for various
NLP tasks. In contrast to the conventional dropout mechanisms which randomly
drop units or connections, the proposed DropHead is a structured dropout
method. It drops entire attention-heads during training and It prevents the
multi-head attention model from being dominated by a small portion of attention
heads while also reduces the risk of overfitting the training data, thus making
use of the multi-head attention mechanism more efficiently. Motivated by recent
studies about the learning dynamic of the multi-head attention mechanism, we
propose a specific dropout rate schedule to adaptively adjust the dropout rate
of DropHead and achieve better regularization effect. Experimental results on
both machine translation and text classification benchmark datasets demonstrate
the effectiveness of the proposed approach.
