Papers by Walter Scheirer
Auto-Sizing the Transformer Network: Improving Speed, Efficiency, and Performance for Low-Resource Machine Translation (D19-56)
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| Challenge: | Neural sequence-to-sequence models are sensitive to architecture and hyperparameter settings. |
| Approach: | They incorporate architecture search into a single training run through auto-sizing . they show that auto-size can improve BLEU scores by up to 3.9 points . |
| Outcome: | The proposed algorithm improves BLEU scores on low-resource language pairs while removing one-third of the parameters from the model. |