| Challenge: | Existing non-autoregressive translation models lack parallel decoding, which is a bottleneck for NMT decoding. |
| Approach: | They propose a framework for non-autoregressive machine translation that emulates the autoregressive model by sampling sentence length in parallel. |
| Outcome: | The proposed model achieves 31.85 BLEU on WMT16 RoEn and 30.68 BLUE on IWSLT16 EnDe on the IWSLD16, WMT14 and WMT15 datasets. |
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| 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. |
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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. |
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Enriching Non-Autoregressive Transformer with Syntactic and Semantic Structures for Neural Machine Translation (2021.eacl-main)
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Non-Autoregressive Neural Machine Translation: A Call for Clarity (2022.emnlp-main)
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| 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. |
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Learning to Rewrite for Non-Autoregressive Neural Machine Translation (2021.emnlp-main)
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| Challenge: | Existing non-autoregressive neural machine translations have poor inference speed but weak recognition of erroneous translation pieces. |
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Hybrid-Regressive Paradigm for Accurate and Speed-Robust Neural Machine Translation (2023.findings-acl)
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| Challenge: | Autoregressive translation (NAT) is less robust in decoding batch size and hardware settings than NAT. |
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Integrating Translation Memories into Non-Autoregressive Machine Translation (2023.eacl-main)
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| Challenge: | Non-autoregressive machine translation (NAT) has made great progress, but most studies focus on standard translation tasks. |
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Non-Autoregressive Document-Level Machine Translation (2023.findings-emnlp)
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Revisiting Non-Autoregressive Translation at Scale (2023.findings-acl)
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| Challenge: | Extensive experiments on two advanced NAT models show scaling can improve translation performance. |
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| Challenge: | Existing non-autoregressive neural machine translation models suffer from poor localization quality due to sequential dependencies within the target sentence. |
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