| Challenge: | Existing non-autoregressive machine translation models have decoders that are difficult to port to NAT models. |
| Approach: | They propose a sequence-to-lattice model that replaces the decoder with a search lattice. |
| Outcome: | The proposed model is faster than past non-autoregressive generation approaches and more accurate than reducing the number of decoder layers. |
Similar Papers
Candidate Soups: Fusing Candidate Results Improves Translation Quality for Non-Autoregressive Translation (2022.emnlp-main)
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| Challenge: | Existing methods to improve NAT model's performance but do not fully utilize it. |
| Approach: | They propose a non-autoregressive translation method which can obtain high-quality translations while maintaining the inference speed of NAT models. |
| Outcome: | The proposed method outperforms the autoregressive translation model on three translation tasks with 7.6 speedup. |
Fully Non-autoregressive Neural Machine Translation: Tricks of the Trade (2021.findings-acl)
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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. |
| Outcome: | The proposed model achieves state-of-the-art results on three translation benchmarks with comparable performance to autoregressive and iterative NAT systems. |
Jointly Masked Sequence-to-Sequence Model for Non-Autoregressive Neural Machine Translation (2020.acl-main)
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| Challenge: | masked language models have been used for natural language processing tasks but few studies have adopted it in the sequence-to-sequence models. |
| Approach: | They propose to combine encoder and decoder to train a masked sequence-to-sequence model . they propose to train the encoder more rigorously by masking the encoded input . |
| Outcome: | The proposed model achieves 27.69/32.24 BLEU scores on English-German/German-English tasks with 5+ times speed up compared with an autoregressive model. |
What Have We Achieved on Non-autoregressive Translation? (2024.findings-acl)
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| Challenge: | Existing studies have shown that non-autoregressive (NAT) methods underperform autoregressive methods (AT) however, their evaluation using BLEU has been shown to weakly correlate with human annotations. |
| Approach: | They propose to evaluate four representative NAT methods using BLEU to narrow the performance gap between autoregressive and autoregressive translations. |
| Outcome: | The proposed methods underperform NAT and autoregressive methods under more reliable evaluation metrics. |
Incorporating a Local Translation Mechanism into Non-autoregressive Translation (2020.emnlp-main)
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| Challenge: | Existing methods to capture local dependencies among output tokens are not efficient, causing errors of repeated translation. |
| Approach: | They propose a local autoregressive translation mechanism that predicts a short sequence of tokens for each target decoding position instead of one token. |
| Outcome: | Empirical results show that the proposed method achieves comparable or better performance with fewer decoding iterations, bringing a 2.5x speedup. |
Tree-Structured Non-Autoregressive Decoding for Sequence-to-Sequence Text Generation (2025.findings-emnlp)
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| Challenge: | Autoregressive Transformers suffer from high inference latency due to sequential token generation. |
| Approach: | They propose a tree-structured non-autoregressive decoding paradigm that bridges autoregressive and non-automatic decoding. |
| Outcome: | The proposed paradigm outperforms autoregressive and non-autoregressive decoding in machine translation and paraphrase generation. |
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. |
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. |
| 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)
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| 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. |
Semi-Autoregressive Neural Machine Translation (D18-1)
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| Challenge: | Existing approaches to neural machine translation are typically autoregressive but suffer from low parallelizability and thus slow at decoding long sequences. |
| Approach: | They propose a semi-autoregressive Transformer model for fast sequence generation that keeps the autoregressive property in global but relieves in local . |
| Outcome: | The proposed model achieves 5.58 speedup while maintaining 88% translation quality, significantly better than previous non-autoregressive methods. |