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The Transformer translation model is based on the multi-head attentionmechanism, which can be parallelized easily. The multi-head attention networkperforms the scaled dot-product attention function in parallel, empowering themodel by jointly attending to information from different representationsubspaces at different positions. In this paper, we present an approach tolearning a hard retrieval attention where an attention head only attends to onetoken in the sentence rather than all tokens. The matrix multiplication betweenattention probabilities and the value sequence in the standard scaleddot-product attention can thus be replaced by a simple and efficient retrievaloperation. We show that our hard retrieval attention mechanism is 1.43 timesfaster in decoding, while preserving translation quality on a wide range ofmachine translation tasks when used in the decoder self- and cross-attentionnetworks.

Learning Hard Retrieval Decoder Attention for Transformers