Papers by Emanuele Rodola
Accelerating Transformer Inference for Translation via Parallel Decoding (2023.acl-long)
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Andrea Santilli, Silvio Severino, Emilian Postolache, Valentino Maiorca, Michele Mancusi, Riccardo Marin, Emanuele Rodola
| Challenge: | Autoregressive decoding limits the efficiency of transformers for Machine Translation (MT) Existing methods to solve this problem are expensive and require changes to the model. |
| Approach: | They propose to reframe autoregressive decoding with a parallel formulation . they propose to speed up existing models without training or modifications while retaining translation quality. |
| Outcome: | The proposed model speeds up existing models without training or modifications while retaining translation quality. |