| Challenge: | Non-autoregressive text to speech models ignore correlation in time and frequency domains, causing blurry results. |
| Approach: | They revisit the problem of over-smoothness in non-autoregressive text to speech models . they use methods that reduce complexity of data distributions and improve modeling methods . |
| Outcome: | The proposed models achieve better voice quality and faster inference speed than autoregressive models. |
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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 . |
Generalized Entropy Regularization or: There’s Nothing Special about Label Smoothing (2020.acl-main)
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| Challenge: | Prior work has explored regularizing the output distributions of probabilistic models to alleviate overfitting. |
| Approach: | They propose a family of entropy regularizers that have a connection to regularization . they find that label smoothing provably does not allow for sparsity in an output distribution . |
| Outcome: | The proposed method improves the relationship between model entropy and performance on language generation tasks. |
CTC-based Non-autoregressive Textless Speech-to-Speech Translation (2024.findings-acl)
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| Challenge: | Existing direct speech-to-speech translation models require text supervision during training, which is not feasible for numerous unwritten languages. |
| Approach: | They propose a non-autoregressive (NAR) model that generates discrete units from the source speech and employs a unit-based vocoder to synthesize the target. |
| Outcome: | The proposed model achieves translation quality comparable to the autoregressive model while preserving up to 26.81 decoding speedup. |
Investigating Ensemble Methods for Model Robustness Improvement of Text Classifiers (2022.findings-emnlp)
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| Challenge: | Existing methods to reduce model's reliance on bias features ignore the learnability of these features. |
| Approach: | They propose to reduce models' reliance on bias features by first training models with fixed low-capacity models which ignore the learnability of the bias features. |
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Non-Autoregressive Text Generation with Pre-trained Language Models (2021.eacl-main)
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| Challenge: | Autoregressive generation models generate tokens in a left-to-right, token-by-token fashion, resulting in lag in inference. |
| Approach: | They propose to use BERT as the backbone of a non-autoregressive generation model for greatly improved performance. |
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Evaluating Open-Source ASR Systems: Performance Across Diverse Audio Conditions and Error Correction Methods (2025.coling-main)
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| Challenge: | Automated speech recognition (ASR) systems are able to transcribe spontaneous human conversations with high accuracy. |
| Approach: | They evaluate the accuracy of open source automatic speech recognition systems across conversational speech datasets and explore the potential of ASR ensembling and post-ASR correction methods to improve transcription accuracy. |
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Speech Translation and the End-to-End Promise: Taking Stock of Where We Are (2020.acl-main)
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| Challenge: | Until recently, the only feasible approach to translating acoustic speech signals into text was the cascaded approach. |
| Approach: | They propose a classification of the main challenges of traditional approaches to speech translation . they argue that end-to-end models fall short due to compromises made to address data scarcity . |
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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. |
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. |
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 . |