mlsanityData to 7 Oct 2026

textitlatent-GLAT: Glancing at Latent Variables for Parallel Text Generation

arXiv 2204.02030 ↗First posted 5 Apr 2022cs.CL cs.AIcited by 20

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Abstract (arXiv)

Recently, parallel text generation has received widespread attention due to its success in generation efficiency. Although many advanced techniques are proposed to improve its generation quality, they still need the help of an autoregressive model for training to overcome the one-to-many multi-modal phenomenon in the dataset, limiting their applications. In this paper, we propose textitlatent-GLAT, which employs the discrete latent variables to capture word categorical information and invoke an advanced curriculum learning technique, alleviating the multi-modality problem. Experiment results show that our method outperforms strong baselines without the help of an autoregressive model, which further broadens the application scenarios of the parallel decoding paradigm.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Builds on 27 of its 39 references that are in the map. First posted on arXiv 5 Apr 2022.

Builds on

27 of 39 references.

References · 27

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15 more
  1. 2019Understanding Knowledge Distillation in Non-autoregressive Machine Translationcited by 121
  2. 2018Fast Decoding in Sequence Models using Discrete Latent Variablescited by 103
  3. 2020Glancing Transformer for Non-Autoregressive Neural Machine Translationcited by 103
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  7. 2020On the Importance of Word and Sentence Representation Learning in Implicit Discourse Relation Classificationcited by 40
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  11. 2021Non-Autoregressive Translation by Learning Target Categorical Codescited by 25
  12. 2020An EM Approach to Non-autoregressive Conditional Sequence Generationcited by 21
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  14. 2019Non-autoregressive Transformer by Position Learningcited by 17
  15. 2021The Volctrans GLAT System: Non-autoregressive Translation Meets WMT21cited by 14

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 6; 2023: 11; 2024: 2; 2025: 0; 2026: 1.

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  1. 2022A Survey on Non-Autoregressive Generation for Neural Machine Translation and Beyondcited by 64
  2. 2022On the Learning of Non-Autoregressive Transformerscited by 27
  3. 2023Conditional Variational Autoencoder for Sign Language Translation with Cross-Modal Alignmentcited by 22
  4. 2022Viterbi Decoding of Directed Acyclic Transformer for Non-Autoregressive Machine Translationcited by 13
  5. 2023CTC-based Non-autoregressive Speech Translationcited by 9
  6. 2023DASpeech: Directed Acyclic Transformer for Fast and High-quality Speech-to-Speech Translationcited by 9
  7. 2023Directed Acyclic Transformer Pre-training for High-quality Non-autoregressive Text Generationcited by 8
  8. 2022Multi-Granularity Optimization for Non-Autoregressive Translationcited by 6
  9. 2023INK: Injecting kNN Knowledge in Nearest Neighbor Machine Translationcited by 6
  10. 2023Non-autoregressive Machine Translation with Probabilistic Context-free Grammarcited by 4
  11. 2022Diffusion Glancing Transformer for Parallel Sequence to Sequence Learningcited by 3
  12. 2024Translating Images to Road Network: A Sequence-to-Sequence Perspectivecited by 3
8 more
  1. 2023Selective Knowledge Distillation for Non-Autoregressive Neural Machine Translationcited by 2
  2. 2022Helping the Weak Makes You Strong: Simple Multi-Task Learning Improves Non-Autoregressive Translatorscited by 1
  3. 2023RenewNAT: Renewing Potential Translation for Non-Autoregressive Transformercited by 1
  4. 2023Shared Latent Space by Both Languages in Non-Autoregressive Neural Machine Translationcited by 1
  5. 2023Non-Autoregressive Document-Level Machine Translationcited by 1
  6. 2024What Have We Achieved on Non-autoregressive Translation?cited by 1
  7. 2023Attentive Multi-Layer Perceptron for Non-autoregressive Generationcited by 0
  8. 2026Towards the Holographic Characteristic of LLMs for Efficient Short-text Generationcited by 0

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  1. 2025A Survey on Parallel Text Generation: From Parallel Decoding to Diffusion Language Models
  2. 2024A Comprehensive Survey of Accelerated Generation Techniques in Large Language Models
  3. 2022A Self-Paced Mixed Distillation Method for Non-Autoregressive Generation
  4. 2026Gumbel Distillation for Parallel Text Generation
  5. 2024Parallelizing Autoregressive Generation with Variational State Space Models
  6. 2022ELMER: A Non-Autoregressive Pre-trained Language Model for Efficient and Effective Text Generation
  7. 2023Diffusion Models for Non-autoregressive Text Generation: A Survey
  8. 2025Speculative Decoding and Beyond: An In-Depth Survey of Techniques