Adaptive Attention for Sparse-based Long-sequence Transformer (2023.findings-acl)
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| Challenge: | Recent studies show that Transformers can process longer sequences because of their complexity and time scales quadratic to the sequence length. |
| Approach: | They propose an efficient Transformer model with adaptive attention that can select useful tokens automatically in sparse attention by learnable position vectors. |
| Outcome: | The proposed model can select useful tokens automatically in sparse attention by learnable position vectors. |
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