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Probing Product Description Generation via Posterior Distillation

AAAI 2021 · Image generation › Autoregressive and latent generation · approach: Synthetic training data from generators · 14 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
use APDT instead of KOBEproduct description generation · Clothes&ShoesROUGE-L: 19.07 -> 20.41Helped
use APDT instead of KOBEproduct description generation · DigitalROUGE-L: 18.97 -> 19.95Helped
use APDT instead of KOBEproduct description generation · HomingROUGE-L: 18.72 -> 19.68Helped
add a copy mechanism (ablation)APDT · Clothes&ShoesROUGE-L: 19.13 -> 20.41Helped
add posterior distillation from reviews (ablation)APDT · Clothes&ShoesROUGE-L: 18.56 -> 20.41Helped
add coherence-enhanced loss (ablation)APDT · Clothes&ShoesROUGE-L: 19.87 -> 20.41Helped
add the copy mechanism, posterior distillation, and CoE loss together (ablation)APDT · Clothes&ShoesROUGE-L: 17.95 -> 20.41Helped
distill from the 5-th or 6-th review encoder layer rather than a shallow layer (ablation)APDT posterior distillation · Clothes&Shoes validationROUGE-LMixed
increase the coherence weight β up to 0.4, then increase it further (ablation)APDT CoE loss · JDPDG validationROUGE-LMixed

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