Data to 7 Oct 2026

AdvAug: Robust Adversarial Augmentation for Neural Machine Translation

arXiv 2006.11834 ↗First posted 21 Jun 2020cs.CLcited by 66

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Abstract (arXiv)

In this paper, we propose a new adversarial augmentation method for Neural Machine Translation (NMT). The main idea is to minimize the vicinal risk over virtual sentences sampled from two vicinity distributions, of which the crucial one is a novel vicinity distribution for adversarial sentences that describes a smooth interpolated embedding space centered around observed training sentence pairs. We then discuss our approach, AdvAug, to train NMT models using the embeddings of virtual sentences in sequence-to-sequence learning. Experiments on Chinese-English, English-French, and English-German translation benchmarks show that AdvAug achieves significant improvements over the Transformer (up to 4.9 BLEU points), and substantially outperforms other data augmentation techniques (e.g. back-translation) without using extra corpora.

Key facts. Cited by 66 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Jun 2020.

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Citing papers per year: 2021: 20; 2022: 18; 2023: 18; 2024: 7; 2025: 2; 2026: 1.

66 papers.

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2021A Survey of Data Augmentation Approaches for NLPcited by 408
2021Regularizing Generative Adversarial Networks under Limited Datacited by 105
2021An Empirical Survey of Data Augmentation for Limited Data Learning in NLPcited by 101
2021Data Augmentation Approaches in Natural Language Processing: A Surveycited by 82
2021Pyramid Adversarial Training Improves ViT Performancecited by 39
2021Faster Meta Update Strategy for Noise-Robust Deep Learningcited by 38
2021Evaluating Saliency Methods for Neural Language Modelscited by 37
2022A Survey of Mix-based Data Augmentation: Taxonomy, Methods, Applications, and Explainabilitycited by 30
2024Large Model Based Agents: State-of-the-Art, Cooperation Paradigms, Security and Privacy, and Future Trendscited by 26
2022A Survey of Adversarial Defences and Robustness in NLPcited by 22
2021Reweighting Augmented Samples by Minimizing the Maximal Expected Losscited by 21
2024A Survey on Mixup Augmentations and Beyondcited by 18

38 more

2022Improving Contrastive Learning of Sentence Embeddings with Case-Augmented Positives and Retrieved Negativescited by 16
2022TreeMix: Compositional Constituency-based Data Augmentation for Natural Language Understandingcited by 15
2022Learning to Generalize to More: Continuous Semantic Augmentation for Neural Machine Translationcited by 13
2021Self-supervised and Supervised Joint Training for Resource-rich Machine Translationcited by 12
2023TransFool: An Adversarial Attack against Neural Machine Translation Modelscited by 12
2023White-Box Multi-Objective Adversarial Attack on Dialogue Generationcited by 12
2021Not Far Away, Not So Close: Sample Efficient Nearest Neighbour Data Augmentation via MiniMaxcited by 10
2022DoubleMix: Simple Interpolation-Based Data Augmentation for Text Classificationcited by 10
2023Targeted Adversarial Attacks against Neural Machine Translationcited by 10
2023Improving the Robustness of Summarization Systems with Dual Augmentationcited by 9
2021As Easy as 1, 2, 3: Behavioural Testing of NMT Systems for Numerical Translationcited by 8
2022BLISS: Robust Sequence-to-Sequence Learning via Self-Supervised Input Representationcited by 8
2023Interdisciplinary Fairness in Imbalanced Research Proposal Topic Inference: A Hierarchical Transformer-based Method with Selective Interpolationcited by 8
2022Multilingual Mix: Example Interpolation Improves Multilingual Neural Machine Translationcited by 7
2022miCSE: Mutual Information Contrastive Learning for Low-shot Sentence Embeddingscited by 7
2023Neural Machine Translation for the Indigenous Languages of the Americas: An Introductioncited by 7
2023DSRM: Boost Textual Adversarial Training with Distribution Shift Risk Minimizationcited by 7
2021Addressing the Vulnerability of NMT in Input Perturbationscited by 6
2022Breaking the Representation Bottleneck of Chinese Characters: Neural Machine Translation with Stroke Sequence Modelingcited by 6
2024Enhancing In-Context Learning via Implicit Demonstration Augmentationcited by 6
2025CluCERT: Certifying LLM Robustness via Clustering-Guided Denoising Smoothingcited by 6
2021TopicRefine: Joint Topic Prediction and Dialogue Response Generation for Multi-turn End-to-End Dialogue Systemcited by 5
2022Data Augmentation for Biomedical Factoid Question Answeringcited by 5
2022Learning to Classify Open Intent via Soft Labeling and Manifold Mixupcited by 5
2023Bridging the Gap between Decision and Logits in Decision-based Knowledge Distillation for Pre-trained Language Modelscited by 5
2024Synthetic Data Generation and Joint Learning for Robust Code-Mixed Translationcited by 5
2021Enhancing Model Robustness By Incorporating Adversarial Knowledge Into Semantic Representationcited by 4
2022Generating Authentic Adversarial Examples beyond Meaning-preserving with Doubly Round-trip Translationcited by 4
2022AdMix: A Mixed Sample Data Augmentation Method for Neural Machine Translationcited by 4
2023MixPro: Simple yet Effective Data Augmentation for Prompt-based Learningcited by 4
2023A Classification-Guided Approach for Adversarial Attacks against Neural Machine Translationcited by 4
2023Investigating Zero- and Few-shot Generalization in Fact Verificationcited by 4
2021Sometimes We Want Translationesecited by 3
2022SingAug: Data Augmentation for Singing Voice Synthesis with Cycle-consistent Training Strategycited by 3
2023Learning to Compose Representations of Different Encoder Layers towards Improving Compositional Generalizationcited by 3
2024Learning under Label Noise through Few-Shot Human-in-the-Loop Refinementcited by 3
2021Visual Cues and Error Correction for Translation Robustnesscited by 2
2021ASAT: Adaptively Scaled Adversarial Training in Time Seriescited by 2

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