mlsanityData to 14 Oct 2026

Non-autoregressive Transformer by Position Learning

arXiv 1911.10677 ↗First posted 25 Nov 2019cs.CL cs.LGcited by 17

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Abstract (arXiv)

Non-autoregressive models are promising on various text generation tasks. Previous work hardly considers to explicitly model the positions of generated words. However, position modeling is an essential problem in non-autoregressive text generation. In this study, we propose PNAT, which incorporates positions as a latent variable into the text generative process. Experimental results show that PNAT achieves top results on machine translation and paraphrase generation tasks, outperforming several strong baselines.

Key facts. Cited by 17 papers in the map (data through 14 Oct 2026). First posted on arXiv 25 Nov 2019.

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Citing papers per year: 2021: 7; 2022: 7; 2023: 2; 2024: 1.

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  1. 2021Position Information in Transformers: An Overviewcited by 103
  2. 2022A Survey on Non-Autoregressive Generation for Neural Machine Translation and Beyondcited by 64
  3. 2022Directed Acyclic Transformer for Non-Autoregressive Machine Translationcited by 44
  4. 2022On the Learning of Non-Autoregressive Transformerscited by 27
  5. 2021Non-Autoregressive Translation by Learning Target Categorical Codescited by 25
  6. 2022textitlatent-GLAT: Glancing at Latent Variables for Parallel Text Generationcited by 20
  7. 2021Sequence-Level Training for Non-Autoregressive Neural Machine Translationcited by 18
  8. 2022Non-Monotonic Latent Alignments for CTC-Based Non-Autoregressive Machine Translationcited by 16
  9. 2021Duplex Sequence-to-Sequence Learning for Reversible Machine Translationcited by 9
  10. 2021NAST: Non-Autoregressive Spatial-Temporal Transformer for Time Series Forecastingcited by 8
  11. 2021NAST: A Non-Autoregressive Generator with Word Alignment for Unsupervised Text Style Transfercited by 7
  12. 2021Modeling Coverage for Non-Autoregressive Neural Machine Translationcited by 4
5 more
  1. 2022Rephrasing the Reference for Non-Autoregressive Machine Translationcited by 3
  2. 2022Diffusion Glancing Transformer for Parallel Sequence to Sequence Learningcited by 3
  3. 2023RenewNAT: Renewing Potential Translation for Non-Autoregressive Transformercited by 1
  4. 2023Attentive Multi-Layer Perceptron for Non-autoregressive Generationcited by 0
  5. 2024NARRepair: Non-Autoregressive Code Generation Model for Automatic Program Repaircited by 0