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Style Transfer from Non-Parallel Text by Cross-Alignment

NeurIPS 2017 · Label-efficient and robust learning › Recognition and long-tail · approach: Multilingual language representation pretraining · 645 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add cross-alignment of transferred sentence populations rather than aligning only latent representations (ablation)Aligned auto-encoder · Word substitution decipherment, 100% substitutionBLEU: 7.2 -> 57.4Helped
add cross-alignment of transferred sentence populations rather than aligning only latent representations (ablation)Aligned auto-encoder · Word substitution decipherment, 20%, 40%, 60%, and 80% substitutionBLEU: 81.0 -> 83.8; 68.9 -> 79.1; 50.7 -> 74.7; 45.6 -> 66.1Helped
add cross-alignment of transferred sentence populations rather than aligning only latent representations (ablation)Aligned auto-encoder · Word order recoveryBLEU: 5.2 -> 26.1Helped
replace cross-alignment with a variational auto-encoder (ablation)Style transfer model · Yelp sentiment modificationsentiment accuracy: 23.2 -> 78.4Hurt
replace cross-alignment with a variational auto-encoder (ablation)Style transfer model · Word substitution decipherment, 100% substitutionBLEU: 0.9 -> 57.4Hurt
replace cross-alignment with a variational auto-encoder (ablation)Style transfer model · Word order recoveryBLEU: 5.3 -> 26.1Hurt
use cross-aligned auto-encoder instead of the control-gen baselineYelp sentiment modification · Yelp sentiment modificationoverall transfer: 41.0 -> 41.5Helped
use cross-aligned auto-encoder instead of the control-gen baselineYelp sentiment modification · Yelp sentiment modificationsentiment accuracy: 83.5 -> 78.4Hurt
use cross-aligned auto-encoder instead of the control-gen baselineYelp sentiment modification · Yelp sentiment modificationfluency: 3.2 -> 2.8Hurt

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