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Tree-Structured Attention with Hierarchical Accumulation

ICLR 2020 · Architectures and efficiency › Attention and transformers · approach: Language model pretraining and induction · 25 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
use tree-based hierarchical attention instead of the strongest listed baselineMachine translation models · IWSLT'14 En-DeBLEU: 28.43 -> 29.47Helped
use tree-based hierarchical attention instead of the strongest listed baselineMachine translation models · IWSLT'13 En-FrBLEU: 43.75 -> 45.53Helped
use tree-based hierarchical attention instead of the strongest listed baselineMachine translation models · IWSLT'14 De-En and IWSLT'13 Fr-EnBLEU: 34.72 -> 35.96; 43.08 -> 44.34Helped
use tree-based hierarchical attention instead of the strongest listed baselineMachine translation models · WMT'14 En-De Base and BigBLEU: 27.48 -> 28.40; 29.70 -> 29.95Helped
use tree-based attention instead of the strongest listed baselineText classification models · SST-5, SST-2, IMDB, and SVAaccuracy: 43.9 -> 47.4; 82.0 -> 84.3; 86.5 -> 90.1; 96.2 -> 98.0Helped
add hierarchical embeddings (ablation)Tree Transformer · IWSLT'14 En-DeBLEU: 29.20 -> 29.47Helped
add hierarchical embeddings (ablation)Tree Transformer · IWSLT'13 En-FrBLEU: 44.80 -> 45.53Helped
add hierarchical embeddings (ablation)Tree Transformer · SST-5accuracy: 46.1 -> 47.4Helped
add subtree masking (ablation)Tree Transformer · IWSLT'14 En-DeBLEU: 29.05 -> 29.47Helped
add subtree masking (ablation)Tree Transformer · IWSLT'13 En-FrBLEU: 45.07 -> 45.53Helped
add subtree masking (ablation)Tree Transformer · SST-5accuracy: 45.7 -> 47.4Helped
add hierarchical embeddings and subtree masking together (ablation)Tree Transformer · IWSLT'14 En-De, IWSLT'13 En-Fr, and SST-5BLEU / accuracy: 28.98 -> 29.47; 44.50 -> 45.53; 45.0 -> 47.4Helped

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