Papers › Deterministic Non-Autoregressive Neural Sequence Modeling by Iterative Refinement
Deterministic Non-Autoregressive Neural Sequence Modeling by Iterative Refinement
Jason Lee, Elman Mansimov, Kyunghyun Cho
We propose a conditional non-autoregressive neural sequence model based on iterative refinement. The proposed model is designed based on the principles of latent variable models and denoising autoencoders, and is generally applicable to any sequence generation task. We extensively evaluate the proposed model on machine translation (En-De and En-Ro) and image caption generation, and observe that it significantly speeds up decoding while maintaining the generation quality comparable to the autoregressive counterpart.
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Code
Syntology Ran 3 of 4 code samples harvested from 2 repositories linked to this paper; 1 has no recorded run. Of those that ran: 1 ran · honoured contract; 1 ran · our draft was wrong; 1 ran with no contract checked.
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Code Syntology ran Syntology
4 samples harvested; 3 ran; 1 honoured the contract we drafted; 1 has no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
Licence: 1 of the 4 samples is pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.
Harvested from 2 repositories linked to this paper, official or community; each sample names its own and says which. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Machine Translation | IWSLT2015 English-German | Denoising autoencoders (non-autoregressive) | BLEU score | 27.01 | #5 of 8 | Archive leaderboard | report |
| Machine Translation | IWSLT2015 German-English | Denoising autoencoders (non-autoregressive) | BLEU score | 32.43 | #5 of 15 | Archive leaderboard | report |
| Machine Translation | WMT2014 English-German | Denoising autoencoders (non-autoregressive) | BLEU score | 21.54 | #75 of 91 | Archive leaderboard | report |
| Machine Translation | WMT2014 German-English | Denoising autoencoders (non-autoregressive) | BLEU score | 25.43 | #12 of 16 | Archive leaderboard | report |
| Machine Translation | WMT2016 English-Romanian | Denoising autoencoders (non-autoregressive) | BLEU score | 29.66 | #10 of 21 | Archive leaderboard | report |
| Machine Translation | WMT2016 Romanian-English | Denoising autoencoders (non-autoregressive) | BLEU score | 30.30 | #19 of 21 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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