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Scaling pre-trained language models has resulted in large performance gainsin various natural language processing tasks but comes with a large cost inmemory requirements. Inspired by the position embeddings in transformers, weaim to simplify and reduce the memory footprint of the multi-head attention(MHA) mechanism. We propose an alternative module that uses only a singleshared projection matrix and multiple head embeddings (MHE), i.e. one per head.We empirically demonstrate that our MHE attention is substantially more memoryefficient compared to alternative attention mechanisms while achieving highpredictive performance retention ratio to vanilla MHA on several downstreamtasks. MHE attention only requires a negligible fraction of additionalparameters ($3nd$, where $n$ is the number of attention heads and $d$ the sizeof the head embeddings) compared to a single-head attention, while MHA requires$(3n^2-3n)d^2-3nd$ additional parameters.

Pit One Against Many: Leveraging Attention-head Embeddings for Parameter-efficient Multi-head Attention