Papers with Non-autoregressive
A Study of Non-autoregressive Model for Sequence Generation (2020.acl-main)
Copied to clipboard
| 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 . |
Reinforcement Learning for Edit-Based Non-Autoregressive Neural Machine Translation (2024.naacl-srw)
Copied to clipboard
| Challenge: | Non-autoregressive (NAR) language models have a performance gap due to the large decoding space and difficulty in capturing dependency between target words accurately. |
| Approach: | They propose to use reinforcement learning to enhance the performance of edit-based NAR models by using stepwise reward maximization and episodic reward maximisation. |
| Outcome: | The proposed model outperforms autoregressive models in the evaluation of an edit-based model. |
Diving into the Decoding Space of Non-Autoregressive Models via Lexically Constrained Search (2026.acl-short)
Copied to clipboard
| Challenge: | Non-autoregressive (NAR) models have been mainly developed to improve decoding efficiency. |
| Approach: | They propose a search-based decoding algorithm which is comparable to the autoregressive Grid Beam Search (GBS) method. |
| Outcome: | The proposed method does not suffer from the MAP degradation issue as the autoregressive method does. |
Improving Non-autoregressive Neural Machine Translation with Monolingual Data (2020.acl-main)
Copied to clipboard
| 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. |