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Learning Multiscale Transformer Models for Sequence Generation

ICML 2022 · Architectures and efficiency › Attention and transformers · approach: Attention head and block redesign · 4 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add linguistic word-boundary and phrase-level interactionsTransformer on WMT'14 En-De · WMT'14 En-De newstest2014, Base and BigBLEU: 27.63 -> 28.51; 29.31 -> 29.75Helped
Add relative positional representationsUMST on WMT'14 En-De · WMT'14 En-De newstest2014, Base and BigBLEU: 28.51 -> 28.90; 29.75 -> 30.15Helped
Add linguistic multiscale interactionsTransformer on WMT'16 En-Ro · WMT'16 En-Ro newstest-2016BLEU: 34.21 -> 34.81Helped
Add linguistic multiscale interactionsTransformer on CNN-DailyMail · CNN-DailyMailROUGE-1: 40.55 -> 41.82Helped
Remove class embedding (ablation)UMST on WMT'14 En-De · WMT'14 En-De newstest2014, depth 6-6 and 12-6BLEU: 28.51 -> 28.39; 29.49 -> 28.99Hurt
Remove intra-group interactions (ablation)UMST on WMT'14 En-De · WMT'14 En-De newstest2014, depth 6-6BLEU: 28.51 -> 27.87Hurt
Remove inter-group interactions (ablation)UMST on WMT'14 En-De · WMT'14 En-De newstest2014, depth 6-6 and 12-6BLEU: 28.51 -> 28.06; 29.49 -> 29.37Hurt
Remove inter-group interactions (ablation)UMST on CNN-DailyMail · CNN-DailyMailROUGE-1: 41.82 -> 41.62Hurt
Replace GCN with average pooling (ablation)UMST on WMT'14 En-De · WMT'14 En-De newstest2014, depth 6-6 and 12-6BLEU: 28.51 -> 27.96; 29.49 -> 28.89Hurt
Replace GCN with GAT (ablation)UMST on WMT'14 En-De · WMT'14 En-De newstest2014, depth 6-6BLEU: 28.51 -> 28.11Hurt
Share the word and phrase GCN projections across blocks, within blocks, or share Q and K (ablation)UMST on WMT'14 En-De · WMT'14 En-De newstest2014BLEU: 28.51 -> 28.33; 28.51 -> 28.39; 28.51 -> 28.41No clear effect
Replace the proposed up-sampling mapping with nearest or linear interpolation (ablation)UMST on WMT'14 En-De · WMT'14 En-De newstest2014BLEU: 28.51 -> 27.96; 28.51 -> 27.61Hurt

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