Papers with hard retrieval attention
Learning Hard Retrieval Decoder Attention for Transformers (2021.findings-emnlp)
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| Challenge: | In this paper, we show that learning a hard retrieval attention that attends to a single token in a sentence is 1.43 times faster than the standard scaled dot-product attention. |
| Approach: | They propose a method to learn hard retrieval attention where an attention head attends to a single token in a sentence rather than all tokens. |
| Outcome: | The proposed method is 1.43 times faster in decoding while preserving translation quality on a wide range of MT tasks. |