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Research map / Probabilistic and scientific modeling / Molecules and proteins

Sequential Latent Knowledge Selection for Knowledge-Grounded Dialogue

ICLR 2020 · Probabilistic and scientific modeling › Molecules and proteins · approach: Cold-start recommendation models · 121 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
use sequential latent knowledge selection instead of the E2E BERT + PostKS + Copy baselineSKT · Wizard of Wikipedia Test Unseenknowledge accuracy: 14.1 -> 18.3Helped
use sequential latent knowledge selection instead of the E2E BERT + PostKS + Copy baselineSKT · Wizard of Wikipedia Test UnseenR-1: 15.6 -> 16.1Helped
add the copy mechanism (ablation)E2E BERT + PostKS · Wizard of Wikipedia Test UnseenR-1: 13.4 -> 15.6Helped
add the copy mechanism (ablation)E2E BERT + PostKS · Wizard of Wikipedia Test Unseenknowledge accuracy: 14.1 -> 14.4No clear effect
add the auxiliary knowledge loss (ablation)SKT · Wizard of Wikipedia Test Seenknowledge accuracy: 0.3 -> 26.8Helped
add the auxiliary knowledge loss (ablation)SKT · Wizard of Wikipedia Test Unseenknowledge accuracy: 0.1 -> 18.3Helped
train with fewer knowledge labels (ablation)SKT · Wizard of Wikipedia Test Seenknowledge accuracy: 26.8 -> 25.1; 26.8 -> 22.4; 26.8 -> 21.0Hurt
train with fewer knowledge labels (ablation)SKT · Wizard of Wikipedia Test Unseenknowledge accuracy: 18.3 -> 16.7; 18.3 -> 13.8; 18.3 -> 12.3Hurt
use BERT-based sentence encoding (ablation)E2E BERT compared with E2E Transformer MemNet · Wizard of Wikipedia Test Seenknowledge accuracy: 22.5 -> 23.7Helped

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