text: 400,199,1516686749170290690,1455258712961167361,Belén Alastruey,Happy to share “On the Locality of Attention in Direct Speech Translation”💬🔍 accepted at the ACL-SRW! We use interpretability techniques to propose an efficient architecture that matches the baseline performance while reducing computational cost. <LINK> <LINK>,https://arxiv.org/abs/2204.09028,"Transformers have achieved state-of-the-art results across multiple NLP tasks. However, the self-attention mechanism complexity scales quadratically with the sequence length, creating an obstacle for tasks involving long sequences, like in the speech domain. In this paper, we discuss the usefulness of self-attention for Direct Speech Translation. First, we analyze the layer-wise token contributions in the self-attention of the encoder, unveiling local diagonal patterns. To prove that some attention weights are avoidable, we propose to substitute the standard self-attention with a local efficient one, setting the amount of context used based on the results of the analysis. With this approach, our model matches the baseline performance, and improves the efficiency by skipping the computation of those weights that standard attention discards. ",On the Locality of Attention in Direct Speech Translation,1,['Happy to share “On the Locality of Attention in Direct Speech Translation”💬🔍 accepted at the ACL-SRW!\n\nWe use interpretability techniques to\npropose an efficient architecture that\nmatches the baseline performance while reducing computational cost.\n\n<LINK> <LINK>'],22,04,260