mlsanityData to 14 Oct 2026

Guiding Non-Autoregressive Neural Machine Translation Decoding with Reordering Information

arXiv 1911.02215 ↗First posted 6 Nov 2019cs.CLcited by 37

Not on the map. Why?

Abstract (arXiv)

Non-autoregressive neural machine translation (NAT) generates each target word in parallel and has achieved promising inference acceleration. However, existing NAT models still have a big gap in translation quality compared to autoregressive neural machine translation models due to the enormous decoding space. To address this problem, we propose a novel NAT framework named ReorderNAT which explicitly models the reordering information in the decoding procedure. We further introduce deterministic and non-deterministic decoding strategies that utilize reordering information to narrow the decoding search space in our proposed ReorderNAT. Experimental results on various widely-used datasets show that our proposed model achieves better performance compared to existing NAT models, and even achieves comparable translation quality as autoregressive translation models with a significant speedup.

Key facts. Cited by 37 papers in the map (data through 14 Oct 2026). First posted on arXiv 6 Nov 2019.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 10; 2022: 10; 2023: 9; 2024: 6; 2025: 1; 2026: 1.

37 papers.

Sort:

By area:

  1. 2021Step-unrolled Denoising Autoencoders for Text Generationcited by 102
  2. 2022A Survey on Non-Autoregressive Generation for Neural Machine Translation and Beyondcited by 64
  3. 2021Rejuvenating Low-Frequency Words: Making the Most of Parallel Data in Non-Autoregressive Translationcited by 34
  4. 2022On the Learning of Non-Autoregressive Transformerscited by 27
  5. 2021Progressive Multi-Granularity Training for Non-Autoregressive Translationcited by 25
  6. 2021AligNART: Non-autoregressive Neural Machine Translation by Jointly Learning to Estimate Alignment and Translatecited by 24
  7. 2021Investigating the Reordering Capability in CTC-based Non-Autoregressive End-to-End Speech Translationcited by 23
  8. 2021Sequence-Level Training for Non-Autoregressive Neural Machine Translationcited by 18
  9. 2022Cross-Lingual Ability of Multilingual Masked Language Models: A Study of Language Structurecited by 17
  10. 2022Non-Monotonic Latent Alignments for CTC-Based Non-Autoregressive Machine Translationcited by 16
  11. 2022Viterbi Decoding of Directed Acyclic Transformer for Non-Autoregressive Machine Translationcited by 13
  12. 2023Candidate Soups: Fusing Candidate Results Improves Translation Quality for Non-Autoregressive Translationcited by 13
25 more
  1. 2021How Does Distilled Data Complexity Impact the Quality and Confidence of Non-Autoregressive Machine Translation?cited by 12
  2. 2023CTC-based Non-autoregressive Speech Translationcited by 9
  3. 2025An Efficient Diffusion-based Non-Autoregressive Solver for Traveling Salesman Problemcited by 9
  4. 2021NAST: A Non-Autoregressive Generator with Word Alignment for Unsupervised Text Style Transfercited by 7
  5. 2021Improving Non-autoregressive Generation with Mixup Trainingcited by 7
  6. 2022Anticipation-Free Training for Simultaneous Machine Translationcited by 5
  7. 2022Non-Autoregressive Neural Machine Translation: A Call for Claritycited by 5
  8. 2023Reinforcement Learning-based Non-Autoregressive Solver for Traveling Salesman Problemscited by 5
  9. 2024Non-autoregressive Sequence-to-Sequence Vision-Language Modelscited by 5
  10. 2021Modeling Coverage for Non-Autoregressive Neural Machine Translationcited by 4
  11. 2022Align, Write, Re-order: Explainable End-to-End Speech Translation via Operation Sequence Generationcited by 4
  12. 2023Non-autoregressive Machine Translation with Probabilistic Context-free Grammarcited by 4
  13. 2022Hybrid-Regressive Neural Machine Translationcited by 3
  14. 2022Rephrasing the Reference for Non-Autoregressive Machine Translationcited by 3
  15. 2024Translating Images to Road Network: A Sequence-to-Sequence Perspectivecited by 3
  16. 2023RenewNAT: Renewing Potential Translation for Non-Autoregressive Transformercited by 1
  17. 2023Shared Latent Space by Both Languages in Non-Autoregressive Neural Machine Translationcited by 1
  18. 2024SBAAM! Eliminating Transcript Dependency in Automatic Subtitlingcited by 1
  19. 2023Attentive Multi-Layer Perceptron for Non-autoregressive Generationcited by 0
  20. 2023Non-Autoregressive Sentence Orderingcited by 0
  21. 2023Quick Back-Translation for Unsupervised Machine Translationcited by 0
  22. 2024Improving Non-autoregressive Machine Translation with Error Exposure and Consistency Regularizationcited by 0
  23. 2024Non-autoregressive Personalized Bundle Generationcited by 0
  24. 2024NARRepair: Non-Autoregressive Code Generation Model for Automatic Program Repaircited by 0
  25. 2026Towards the Holographic Characteristic of LLMs for Efficient Short-text Generationcited by 0