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Non-Autoregressive Neural Machine Translation with Enhanced Decoder Input

AAAI 2019 · Architectures and efficiency › RNNs and normalization · approach: Non-autoregressive translation · 119 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add enhanced target-side decoder inputs using phrase-table lookup or embedding mappingNART · WMT14 En-DeBLEU: 19.17 -> 24.28Helped
Add enhanced target-side decoder inputs using phrase-table lookup or embedding mappingNART · WMT16 En-RoBLEU: 29.79 -> 34.51Helped
Use phrase-table lookup rather than word-table lookup for decoder inputENAT · WMT14 En-DeBLEU: 19.16 -> 20.33Helped
Add word-level adversary loss L_adv to sentence-level alignment L_align (ablation)ENAT Embedding Mapping · IWSLT14 De-EnBLEU: 23.74 -> 24.13Helped
Add sentence-level alignment loss L_align to word-level adversary loss L_adv (ablation)ENAT Embedding Mapping · IWSLT14 De-EnBLEU: 23.53 -> 24.13Helped
Use embedding mapping rather than phrase-table lookupENAT · WMT14 En-DeBLEU: 20.26 vs. 20.65; rescoring: 23.22 vs. 24.28Mixed
Use embedding mapping rather than phrase-table lookupENAT · IWSLT14 De-EnBLEU: 25.09 -> 24.13Hurt
Use embedding mapping rather than phrase-table lookupENAT · WMT16 En-RoBLEU: 29.85 -> 30.08; rescoring: 34.04 -> 34.51Helped
Use phrase-table lookup rather than embedding mappingENAT · WMT14 De-EnBLEU: 23.02 -> 23.23; rescoring: 26.10 -> 26.67Helped
Increase decoder-input quality from word-table to phrase-table lookupENAT · WMT14 En-DeBLEU: 3.54 -> 6.03; 19.16 -> 20.33Helped

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