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Multi-head self-attention is a key component of the Transformer, astate-of-the-art architecture for neural machine translation. In this work weevaluate the contribution made by individual attention heads in the encoder tothe overall performance of the model and analyze the roles played by them. Wefind that the most important and confident heads play consistent and oftenlinguistically-interpretable roles. When pruning heads using a method based onstochastic gates and a differentiable relaxation of the L0 penalty, we observethat specialized heads are last to be pruned. Our novel pruning method removesthe vast majority of heads without seriously affecting performance. Forexample, on the English-Russian WMT dataset, pruning 38 out of 48 encoder headsresults in a drop of only 0.15 BLEU.

Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned