Papers with iterative NAR models

2 papers
Isotropy-Enhanced Conditional Masked Language Models (2023.findings-emnlp)

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Challenge: Existing non-autoregressive models with auto-regressing decoding paradigms have been used for various text generation tasks to accelerate inference but at the cost of generation quality to some extent.
Approach: They propose to use Look Neighbors strategy to enhance learning of target token representations during training to achieve a good balance between inference speedup and generation quality.
Outcome: The proposed models outperform current models on 4 WMT datasets and outperformed the current SoTA results.
An Empirical Study of Iterative Refinements for Non-autoregressive Translation (2025.acl-long)

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Challenge: Iterative non-autoregressive (NAR) models have recently demonstrated impressive performance in varied generation tasks, surpassing the autoregressive Transformer.
Approach: They propose a strategy to conduct efficient refinements without performance declines by using two simple metrics to identify potential problems existing in current refinement processes.
Outcome: The proposed model outperforms the autoregressive Transformer by around one BLEU on average.

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