Iterative Refinement in the Continuous Space for Non-Autoregressive Neural Machine Translation (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing non-autoregressive inference procedures that refine in token space often require computational overhead. |
| Approach: | They propose an efficient inference procedure that iteratively refines translation purely in the continuous space using a latent variable instead of the latent variables. |
| Outcome: | The proposed procedure is twice as efficient and more effective than the existing EM-like inference procedure. |
Similar Papers
Fully Non-autoregressive Neural Machine Translation: Tricks of the Trade (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing non-autoregressive neural machine translation models are slow to learn the dependency between output tokens. |
| Approach: | They propose to use fully non-autoregressive neural machine translation (NAT) to predict tokens with single forward of neural networks. |
| Outcome: | The proposed model achieves state-of-the-art results on three translation benchmarks with comparable performance to autoregressive and iterative NAT systems. |
An Empirical Study of Iterative Refinements for Non-autoregressive Translation (2025.acl-long)
Copied to clipboard
| 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. |
Deterministic Non-Autoregressive Neural Sequence Modeling by Iterative Refinement (D18-1)
Copied to clipboard
| 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. |
Learning to Rewrite for Non-Autoregressive Neural Machine Translation (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing non-autoregressive neural machine translations have poor inference speed but weak recognition of erroneous translation pieces. |
| Approach: | They propose an architecture to explicitly learn to rewrite the erroneous translation pieces. |
| Outcome: | The proposed architecture can achieve better performance while significantly reducing decoding time. |
Non-Autoregressive Neural Machine Translation: A Call for Clarity (2022.emnlp-main)
Copied to clipboard
| Challenge: | Non-autoregressive translation models require a single forward pass to generate the output sequence instead of iteratively producing each predicted token. |
| Approach: | They propose to use a single forward pass to generate the output sequence instead of iteratively producing each predicted token. |
| Outcome: | The proposed models improve translation quality and speed under third-party testing environments. |
Hybrid-Regressive Paradigm for Accurate and Speed-Robust Neural Machine Translation (2023.findings-acl)
Copied to clipboard
| Challenge: | Autoregressive translation (NAT) is less robust in decoding batch size and hardware settings than NAT. |
| Approach: | They propose a two-stage translation prototype that prompts a small number of AT predictions and fills in previously skipped tokens at once. |
| Outcome: | The proposed translation prototype achieves comparable translation quality with AT while having 1.5x faster inference speed regardless of batch size and device. |
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. |
Align-Refine: Non-Autoregressive Speech Recognition via Iterative Realignment (2021.naacl-main)
Copied to clipboard
| Challenge: | Non-autoregressive encoder-decoder models improve decoding speed, but generation quality suffers . editing at the level of output sequences limits model flexibility. |
| Approach: | They propose *iterative realignment* which iteratively realigns connectionist temporal alignments. |
| Outcome: | The proposed model matches an autoregressive baseline with a 14x speedup on the WSJ dataset; on LibriSpeech, it achieves an LM-free test-other WER of 9.0% (19% relative improvement on comparable work). |
End-to-End Non-Autoregressive Neural Machine Translation with Connectionist Temporal Classification (D18-1)
Copied to clipboard
| Challenge: | Autoregressive decoding is the only part of sequence-to-sequence models that prevents massive parallelization at inference time. |
| Approach: | They propose a non-autoregressive architecture based on connectionist temporal classification . they conduct experiments on the WMT English-Romanian and English-German datasets . |
| Outcome: | The proposed model achieves a significant speedup over autoregressive models . the model can be trained end-to-end and maintains translation quality comparable to other models compared to autoregression models based on connectionist temporal classification . |
Enriching Non-Autoregressive Transformer with Syntactic and Semantic Structures for Neural Machine Translation (2021.eacl-main)
Copied to clipboard
| Challenge: | Existing non-autoregressive models have boosted the efficiency of neural machine translation, but their performance is significantly worse than that of autoregressive counterparts. |
| Approach: | They propose to incorporate syntactic and semantic structures among natural languages into a non-autoregressive Transformer for the task of neural machine translation. |
| Outcome: | The proposed model achieves faster speed and keeps translation quality compared with other models. |