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Deliberation Networks: Sequence Generation Beyond One-Pass Decoding
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add a second-pass deliberation decoder on top of the first-pass draft | RNNSearch shallow NMT · WMT14 En→Fr newstest2014 | BLEU: +1.7 over Mbase (29.97 to 31.67) | Helped |
| add a second-pass deliberation decoder | RNNSearch shallow NMT · Zh→En NIST04-08 | BLEU: average gain of 1.26 over Mbase | Helped |
| add a second decoding layer without deliberation (Mdec×2) (ablation) | RNNSearch shallow NMT · WMT14 En→Fr newstest2014 | BLEU: +0.43 over Mbase (30.40 vs 29.97) | Helped |
| add a review network on the encoder side (4 reviewers) (ablation) | RNNSearch shallow NMT · WMT14 En→Fr newstest2014 | BLEU: +0.79 over Mbase (30.76 vs 29.97); deliberation is 0.91 better | Helped |
| add deliberation network on top of dual learning (ablation) | deep 4-layer LSTM NMT · WMT14 En→Fr | BLEU: +0.97 over dual learning (40.53 to 41.50) | Helped |
| add a second-pass deliberation decoder | RNNSearch summarization on Gigaword · Gigaword test | ROUGE-1: +3.45 over Mbase (27.45 to 30.90) | Helped |
| add a second-pass deliberation decoder | RNNSearch summarization on Gigaword · Gigaword test | ROUGE-2: +1.70 over Mbase (10.51 to 12.21) | Helped |
| add a second-pass deliberation decoder | RNNSearch summarization on Gigaword · Gigaword test | ROUGE-L: +3.02 over Mbase (26.07 to 29.09) | Helped |
| deliberation decoding roughly doubles decoding time | RNNSearch shallow NMT · 3003 En→Fr sentences decoding time | decoding time (s): 964 s to 1924 s | Hurt |
Closest papers by meaning
- Levenshtein Transformer (NeurIPS 2019 · similarity 0.86)
- MacNet: Transferring Knowledge from Machine Comprehension to Sequence-to-Sequence Models (NeurIPS 2018 · similarity 0.86)
- Syntactically Look-Ahead Attention Network for Sentence Compression (AAAI 2020 · similarity 0.86)
- MASS: Masked Sequence to Sequence Pre-training for Language Generation (ICML 2019 · similarity 0.86)
- DiffuSeq: Sequence to Sequence Text Generation with Diffusion Models (ICLR 2023 · similarity 0.85)
- Multi-Agent Discussion Mechanism for Natural Language Generation (AAAI 2019 · similarity 0.85)
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