| Challenge: | et al., 2017) is the most prevailing neural architecture for sequence-to-sequence learning. |
| Approach: | They propose to solve for the equilibrium state of NAR models with black-box root-finding solvers and back-propagate through the equilibrium point via implicit differentiation with constant memory. |
| Outcome: | The proposed framework can converge to a more accurate prediction on four WMT benchmarks. |
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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. |
Improving Non-autoregressive Neural Machine Translation with Monolingual Data (2020.acl-main)
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| Challenge: | Neural machine translation is usually done via knowledge distillation from an autoregressive (AR) model. |
| Approach: | They leverage large monolingual corpora to improve the NAR model's performance by transferring the autoregressive model' s generalization ability while preventing overfitting. |
| Outcome: | The proposed methods on the WMT14 En-De and WMT16 En-Ro news translation tasks show that monolingual data augmentation improves the NAR model to approach the teacher AR model’s performance. |
Non-Autoregressive Sequence Generation (2022.acl-tutorials)
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| Challenge: | Non-autoregressive sequence generation (NAR) models generate output sequences in parallel to speed up generation process. |
| Approach: | This tutorial provides a thorough introduction and review of non-autoregressive sequence generation . it aims to generate the entire or partial output sequences in parallel to speed up the generation process . |
| Outcome: | This tutorial provides a thorough introduction and review of non-autoregressive sequence generation . it aims to reduce the performance gap between state-of-the-art models due to lack of modeling power . |
A Study of Non-autoregressive Model for Sequence Generation (2020.acl-main)
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| Challenge: | Non-autoregressive (NAR) models generate all tokens in parallel, resulting in faster generation speed compared to autoregressive models. |
| Approach: | They propose to use knowledge distillation and source-target alignment to bridge the gap between NAR and autoregressive models in various tasks. |
| Outcome: | The proposed techniques can speed up NAR models in some tasks but not all . the proposed techniques reduce target token dependency while allowing for faster inference . |
Deterministic Non-Autoregressive Neural Sequence Modeling by Iterative Refinement (D18-1)
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| Challenge: | Despite its success, neural autoregressive modeling has its weakness in decoding, i.e., finding the most likely sequence. |
| Approach: | They propose a conditional non-autoregressive neural sequence model based on iterative refinement based upon latent variable models and conditional denoising autoencoders. |
| Outcome: | The proposed model significantly speeds up decoding while maintaining the generation quality comparable to the autoregressive counterpart. |
Non-Autoregressive Models for Fast Sequence Generation (2022.emnlp-tutorials)
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| Challenge: | Autoregressive (AR) models can only generate target sequence word-by-word due to the AR mechanism and suffer from slow inference. |
| Approach: | This tutorial provides an introduction to non-autoregressive sequence generation. |
| Outcome: | This tutorial explains how to generate non-autoregressive sequence generation models. |
NAG-NER: a Unified Non-Autoregressive Generation Framework for Various NER Tasks (2023.acl-industry)
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| Challenge: | Existing models for general NER tasks require entities to be generated in a predefined order, causing error propagation and inefficient decoding. |
| Approach: | They propose a non-autoregressive generation framework for general NER tasks that generates entities as a set instead of a sequence, avoiding error propagation and inefficient decoding. |
| Outcome: | The proposed model outperforms state-of-the-art models on three benchmark NER datasets and two of our proprietary NER tasks. |
Non-Autoregressive Machine Translation: It’s Not as Fast as it Seems (2022.naacl-main)
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| Challenge: | Efficient machine translation models are commercially important as they can increase inference speeds, reduce costs and carbon emissions. |
| Approach: | They compare NAR models with autoregressive models to evaluate their performance . they point out flaws in evaluation methodology and argue for consistent evaluation . |
| Outcome: | The proposed model is faster on GPUs, but slower under more realistic usage conditions. |
Leveraging Relaxed Equilibrium by Lazy Transition for Sequence Modeling (2022.acl-long)
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| Challenge: | Using attention-based models, certain tokens are less ambiguous than others, and they require fewer refinements for disambiguation. |
| Approach: | They propose a lazy transition mechanism to adjust the significance of iterative refinements for each token representation. |
| Outcome: | The proposed model outperforms baseline models on several tasks with the same number of parameters. |
Helping the Weak Makes You Strong: Simple Multi-Task Learning Improves Non-Autoregressive Translators (2022.emnlp-main)
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| Challenge: | Non-autoregressive (NAR) neural machine translation models require a conditional independence assumption on target sequences, resulting in less informative learning signals. |
| Approach: | They propose a model-agnostic multi-task learning framework to provide more informative learning signals for NAR models under conventional MLE training. |
| Outcome: | The proposed framework improves accuracy of multiple NAR baselines without additional decoding overhead. |