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Non-Autoregressive Neural Machine Translation with Enhanced Decoder Input
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add enhanced target-side decoder inputs using phrase-table lookup or embedding mapping | NART · WMT14 En-De | BLEU: 19.17 -> 24.28 | Helped |
| Add enhanced target-side decoder inputs using phrase-table lookup or embedding mapping | NART · WMT16 En-Ro | BLEU: 29.79 -> 34.51 | Helped |
| Use phrase-table lookup rather than word-table lookup for decoder input | ENAT · WMT14 En-De | BLEU: 19.16 -> 20.33 | Helped |
| Add word-level adversary loss L_adv to sentence-level alignment L_align (ablation) | ENAT Embedding Mapping · IWSLT14 De-En | BLEU: 23.74 -> 24.13 | Helped |
| Add sentence-level alignment loss L_align to word-level adversary loss L_adv (ablation) | ENAT Embedding Mapping · IWSLT14 De-En | BLEU: 23.53 -> 24.13 | Helped |
| Use embedding mapping rather than phrase-table lookup | ENAT · WMT14 En-De | BLEU: 20.26 vs. 20.65; rescoring: 23.22 vs. 24.28 | Mixed |
| Use embedding mapping rather than phrase-table lookup | ENAT · IWSLT14 De-En | BLEU: 25.09 -> 24.13 | Hurt |
| Use embedding mapping rather than phrase-table lookup | ENAT · WMT16 En-Ro | BLEU: 29.85 -> 30.08; rescoring: 34.04 -> 34.51 | Helped |
| Use phrase-table lookup rather than embedding mapping | ENAT · WMT14 De-En | BLEU: 23.02 -> 23.23; rescoring: 26.10 -> 26.67 | Helped |
| Increase decoder-input quality from word-table to phrase-table lookup | ENAT · WMT14 En-De | BLEU: 3.54 -> 6.03; 19.16 -> 20.33 | Helped |
Closest papers by meaning
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- Non-Autoregressive Neural Machine Translation (ICLR 2018 · similarity 0.91)
- Rephrasing the Reference for Non-autoregressive Machine Translation (AAAI 2023 · similarity 0.91)
- AEQA-NAT : Adaptive End-to-end Quantization Alignment Training Framework for Non-autoregressive Machine Translation (ICML 2025 · similarity 0.90)
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