Papers with NAT
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. |
MR-P: A Parallel Decoding Algorithm for Iterative Refinement Non-Autoregressive Translation (2022.findings-acl)
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| Challenge: | Non-autoregressive neural machine translation models remove dependency between tokens in the target sentence and generate all tokens on parallel . |
| Approach: | They propose a non-autoregressive neural machine translation model that decodes with the Mask-Predict algorithm which iteratively refines the output. |
| Outcome: | The proposed algorithm increases the performance of the WMT’14 translation task by 1.39 points. |
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. |
Non-Autoregressive Neural Machine Translation with Consistency Regularization Optimized Variational Framework (2022.naacl-main)
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| Challenge: | Variational Autoencoder (VAE) is an effective framework to model the interdependency for non-autoregressive neural machine translation (NAT). |
| Approach: | They propose to use Variational Autoencoder to model interdependency for non-autoregressive neural machine translation (NAT) a posterior consistency regularization approach is proposed to improve translation quality . |
| Outcome: | The proposed model is 1.5/0.7 and 0.8/0.3 BLEU points faster than the baseline model. |
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. |
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. |
| Approach: | They propose to train an edit-based NAT model with a Translation Memory (TM) they propose to modify the data presentation and introduce an extra deletion operation to reduce decoding load. |
| Outcome: | The proposed model performs on par with an autoregressive approach while reducing the decoding load. |
Imitation Learning for Non-Autoregressive Neural Machine Translation (P19-1)
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| 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. |
A Study of Syntactic Multi-Modality in Non-Autoregressive Machine Translation (2022.naacl-main)
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| Challenge: | Non-autoregressive translation models suffer from the multi-modality problem when a source sentence corresponds to multiple correct translations. |
| Approach: | They propose to decompose the syntactic multi-modality problem into short- and long-range models and evaluate them on synthesized and real datasets. |
| Outcome: | The proposed loss functions can handle short- and long-range syntactic multi-modalities better than existing models. |
NAT: Noise-Aware Training for Robust Neural Sequence Labeling (2020.acl-main)
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| Challenge: | Sequence labeling systems should perform reliably under ideal conditions and with corrupted inputs. |
| Approach: | They propose two noise-aware training objectives that improve robustness of sequence labeling performed on perturbed inputs. |
| Outcome: | The proposed methods improve robustness on English and German named entity recognition benchmarks. |
Glancing Transformer for Non-Autoregressive Neural Machine Translation (2021.acl-long)
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| Challenge: | Existing non-autoregressive neural machine translation methods are either inferior to Transformer or require multiple decoding passes, leading to reduced speedup. |
| Approach: | They propose a Glancing Language Model (GLM) for single-pass parallel generation models and Glancing Transformer (GLAT) with only single- pass decoding, GLAT is able to generate high-quality translation with 8-15 speedup. |
| Outcome: | The proposed model outperforms all previous non-autoregressive methods on multiple language directions and is nearly comparable to Transformer. |
Redistributing Low-Frequency Words: Making the Most of Monolingual Data in Non-Autoregressive Translation (2022.acl-long)
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| Challenge: | Knowledge distillation (KD) is the preliminary step for training non-autoregressive translation models, but it can lose important information for translating low-frequency words. |
| Approach: | They propose a knowledge distillation method which trains NAT student on external monolingual data with AT teacher trained on the original bilingual data. |
| Outcome: | Extensive experiments on eight WMT benchmarks show that monolingual KD outperforms the standard KD by improving low-frequency word translation without introducing any computational cost. |
Assessing Non-autoregressive Alignment in Neural Machine Translation via Word Reordering (2022.findings-emnlp)
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| Challenge: | Existing non-autoregressive neural machine translation models that implicitly model dependencies are sub-optimal in handling word order errors. |
| Approach: | They propose to learn a non-autoregressive language model that can be combined with Viterbi decoding to achieve better reordering performance. |
| Outcome: | The proposed model outperforms state-of-the-art reordering mechanisms under different word permutation settings with a 2-27 BLEU improvement, suggesting high potential for word alignment in NAT. |
Progressive Multi-Granularity Training for Non-Autoregressive Translation (2021.findings-acl)
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| Challenge: | Non-autoregressive translation models are weak at learning high-mode knowledge, argues a new study . despite the improved learning difficulty, there are still complicated word orders and structures in the synthetic sentences, making the NAT performance sub-optimal. |
| Approach: | They propose to train non-autoregressive translation models to learn fine-grained lower-mode knowledge . they break down sentence-level examples into three types and increase granularities . |
| Outcome: | The proposed method improves phrase translation accuracy and model reordering ability against strong NAT baselines. |
Rejuvenating Low-Frequency Words: Making the Most of Parallel Data in Non-Autoregressive Translation (2021.acl-long)
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| Challenge: | Knowledge distillation (KD) is commonly used to construct synthetic data for training non-autoregressive translation models. |
| Approach: | They propose to use knowledge distillation to generate training data for non-autoregressive translation models by leveraging pretraining. |
| Outcome: | The proposed approach achieves 28.2 and 33.9 BLEU points on the WMT14 English-German and WMT16 Romanian-English datasets. |
Learning to Recover from Multi-Modality Errors for Non-Autoregressive Neural Machine Translation (2020.acl-main)
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| Challenge: | Existing non-autoregressive neural machine translation models suffer from multi-modality problem . despite their autoregressivity, most NMT models suffer with slow decoding speed . |
| Approach: | They propose a semi-autoregressive model which generates a translation as a sequence of segments while each segment is predicted token-by-token. |
| Outcome: | The proposed model can achieve 4 times speedup while maintaining comparable performance. |
One Reference Is Not Enough: Diverse Distillation with Reference Selection for Non-Autoregressive Translation (2022.naacl-main)
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| Challenge: | Existing non-autoregressive neural machine translation models suffer from multimodality problem . multi-modality is not solved by a teacher forcing algorithm, limiting model capability . |
| Approach: | They propose a method that generates multiple reference translations for each source sentence . they compare the NAT output with all references and select the one that best fits the simulated model . |
| Outcome: | The proposed method achieves 29.82 BLEU with only one decoding pass on WMT14 En-De . |
Retrieving Sequential Information for Non-Autoregressive Neural Machine Translation (P19-1)
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| Challenge: | Experimental results show that the Reinforce-NAT system surpasses the baseline NAT system by a significant margin on BLEU without decelerating the decoding speed. |
| Approach: | They propose a sequence-level training method and a Transformer decoder to fuse the target sequential information into the top layer of the decoded Transformer. |
| Outcome: | The proposed model surpasses the baseline NAT system on BLEU without decelerating the decoding speed and achieves comparable translation performance to the autoregressive Transformer model with considerable speedup. |
Multi-Task Learning with Shared Encoder for Non-Autoregressive Machine Translation (2021.naacl-main)
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| Challenge: | Existing non-autoregressive machine translation models have shown significant inference speedup but suffer from inferior translation accuracy. |
| Approach: | They propose to use AT as an auxiliary task to transfer AT knowledge to NAT models by knowledge distillation. |
| Outcome: | The proposed method achieves significant improvements over baseline non-Autoregressive machine translation models on WMT14 En-De and WMT16 En-Ro datasets. |
Sequence-to-Lattice Models for Fast Translation (2021.findings-emnlp)
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| 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. |
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. |
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. |
Multi-Granularity Optimization for Non-Autoregressive Translation (2022.emnlp-main)
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| Challenge: | Non-autoregressive machine translation suffers severe performance deterioration due to the naive independence assumption. |
| Approach: | They propose a method which collects model behaviours on translation segments of various granularities and integrates feedback for backpropagation to reduce latency. |
| Outcome: | Experiments on four benchmark datasets show that the proposed method outperforms baseline models trained with cross-entropy loss and achieves the best performance on WMT’16 EnRo and highly competitive results on WTM’14 EnDe. |
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. |
NAT: Enhancing Agent Tuning with Negative Samples (2025.naacl-long)
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| Challenge: | Existing methods for fine-tuning and reinforcement learning use only positive examples, limiting their efficiency in low-resource scenarios. |
| Approach: | They propose a method that leverages both successful and failed trajectories for fine-tuning, maximizing the utility of limited resources. |
| Outcome: | The proposed method surpasses existing methods, including SFT, DPO, and PPO, across various tasks. |
Context-Aware Cross-Attention for Non-Autoregressive Translation (2020.coling-main)
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| Challenge: | Existing studies have shown that non-autoregressive translation models can predict all tokens independently and simultaneously. |
| Approach: | They propose to enhance signals of neighbour source tokens into conventional cross-attention to address a locality perception problem in NAT cross- attention. |
| Outcome: | The proposed approach improves translation quality over strong NAT baselines on representative datasets. |
Neighbors Are Not Strangers: Improving Non-Autoregressive Translation under Low-Frequency Lexical Constraints (2022.naacl-main)
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| Challenge: | Existing approaches to lexically constrained neural machine translation suffer from high latency. |
| Approach: | They propose a plug-in algorithm for non-autoregressive translation for this problem . they propose ACT to familiarize the model with the source-side context of constraints . |
| Outcome: | The proposed model improves over the backbone constrained NAT model in constraint preservation and translation quality, especially for rare constraints. |
ngram-OAXE: Phrase-Based Order-Agnostic Cross Entropy for Non-Autoregressive Machine Translation (2022.coling-1)
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| Challenge: | Recent studies have incorporated approaches to improving the standard cross-entropy loss to ameliorate the effect of multimodality. |
| Approach: | They propose a new training oaxe loss which removes the penalty of word order errors in the standard cross-entropy loss. |
| Outcome: | Extensive experiments on NAT benchmarks show that the proposed approach improves translation quality and improves model performance. |
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. |
Con-NAT: Contrastive Non-autoregressive Neural Machine Translation (2022.findings-emnlp)
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| Challenge: | Neural machine translation models are autoregressive, which means they predict tokens one by one based on source tokens and previously predicted tokens. |
| Approach: | They propose a conditional masked language model which incorporates contrastive learning into the conditional language model. |
| Outcome: | The proposed model improves on WMT’16 Ro-En translation directions with different data sizes. |
Improving Non-Autoregressive Neural Machine Translation via Modeling Localness (2022.coling-1)
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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. |
| Approach: | They propose to introduce local information into NAT models by explicitly introducing local information about surrounding words into the encoder and decoder sides to achieve localness-aware representations. |
| Outcome: | The proposed method can achieve significant improvements over strong NAT baselines. |
XLM-D: Decorate Cross-lingual Pre-training Model as Non-Autoregressive Neural Machine Translation (2022.emnlp-main)
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| Challenge: | Existing pre-training language models have been successful in natural language understanding and autoregressive generation tasks, but non-autoregressive models have not been sufficiently successful. |
| Approach: | They propose a pre-trained masked language model (MLM) and a non-autoregressive generation model with a lightweight decorator. |
| Outcome: | The proposed model outperforms the previous mask-predict model on translation datasets by 19.9x. |
Non-Autoregressive Machine Translation as Constrained HMM (2024.findings-acl)
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| Challenge: | Autoregressive (AR) models have some drawbacks due to slow inference speed and label bias due to local normalization. |
| Approach: | They propose to use a left-to-right Hidden Markov Model (HMM) to control label bias in non-autoregressive translation (NAT) They propose a bi-directional HMM, which can regularize each other's biases via shared parameters. |
| Outcome: | The proposed models can achieve comparable performance to autoregressive Transformers using various decoding methods. |
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. |
| Approach: | They empirically examine the impact of scaling on NAT behaviors on a large-scale WMT dataset. |
| Outcome: | The proposed model can achieve comparable performance with the scaling model while maintaining the superiority of decoding speed with standard NAT models. |
Efficient Domain Adaptation for Non-Autoregressive Machine Translation (2024.findings-acl)
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| Challenge: | Existing non-parametric approaches like nearest neighbor machine translation have made small Autoregressive translation models less efficient . despite their impressive generalization and task performance, LLMs suffer from prohibitive inference cost when confronted with specific domains. |
| Approach: | They propose a domain adaptation approach that tailors a k-nearest-neighbor algorithm for NAT models that incorporates the parallel nature of NAT. |
| Outcome: | The proposed approach achieves significant improvements over the Base-NAT model and exhibits enhanced efficiency. |
Self-Improvement of Non-autoregressive Model via Sequence-Level Distillation (2023.emnlp-main)
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| Challenge: | Existing non-autoregressive Transformers (NAT) models generate the entire sequence in parallel, but the multimodality problem limits their performance. |
| Approach: | They propose a method to generate distilled data by the NAT model itself, eliminating the need for additional teacher networks. |
| Outcome: | The proposed method can generate distilled data by the NAT model without teacher networks and adapt to different NAT models without precise adjustments. |
Non-Autoregressive Document-Level Machine Translation (2023.findings-emnlp)
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| Challenge: | Existing non-autoregressive translation models struggle with document context and handling discourse phenomena. |
| Approach: | They propose a simple but effective design of sentence alignment between source and target to improve their performance on document-level machine translation. |
| Outcome: | The proposed model achieves high acceleration on documents and sentence alignment significantly enhances their performance. |