Selective Attention Boosts Transformer Performance On Language Tasks
Selective Attention boosts Transformer performance by 0.5-2.0 BLEU points on tasks like machine translation & question answering. It selectively attends to a subset of input elements, improving efficiency & accuracy.
This is a Plain English Papers summary of a research paper called Selective Attention Boosts Transformer Performance on Language Tasks. If you like these kinds of analysis, you should join AImodels.fyi or follow me on Twitter. Overview This research paper proposes a new attention mechanism called Selective Attention that improves the performance of Transformer models. The key idea is to selectively attend to a subset of input elements rather than all elements, which can lead to more efficient and effective information processing. The authors demonstrate the benefits of Selective Atte...