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Sequence-Level Mixed Sample Data Augmentation

arXiv 2011.09039 ↗First posted 18 Nov 2020cs.CL cs.LGcited by 71

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Abstract (arXiv)

Despite their empirical success, neural networks still have difficulty capturing compositional aspects of natural language. This work proposes a simple data augmentation approach to encourage compositional behavior in neural models for sequence-to-sequence problems. Our approach, SeqMix, creates new synthetic examples by softly combining input/output sequences from the training set. We connect this approach to existing techniques such as SwitchOut and word dropout, and show that these techniques are all approximating variants of a single objective. SeqMix consistently yields approximately 1.0 BLEU improvement on five different translation datasets over strong Transformer baselines. On tasks that require strong compositional generalization such as SCAN and semantic parsing, SeqMix also offers further improvements.

Key facts. Cited by 71 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Nov 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 17; 2022: 18; 2023: 19; 2024: 12; 2025: 4; 2026: 1.

71 papers.

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  1. 2021A Survey of Data Augmentation Approaches for NLPcited by 408
  2. 2021GPT3Mix: Leveraging Large-scale Language Models for Text Augmentationcited by 185
  3. 2023Increasing Diversity While Maintaining Accuracy: Text Data Generation with Large Language Models and Human Interventionscited by 121
  4. 2021A Survey on Data Augmentation for Text Classificationcited by 111
  5. 2021An Empirical Survey of Data Augmentation for Limited Data Learning in NLPcited by 101
  6. 2022State-of-the-art generalisation research in NLP: A taxonomy and reviewcited by 97
  7. 2021Improving Compositional Generalization with Latent Structure and Data Augmentationcited by 46
  8. 2023On Robustness of Prompt-based Semantic Parsing with Large Pre-trained Language Model: An Empirical Study on Codexcited by 41
  9. 2021FlipDA: Effective and Robust Data Augmentation for Few-Shot Learningcited by 32
  10. 2021Learning Algebraic Recombination for Compositional Generalizationcited by 31
  11. 2021Sequence-to-Sequence Learning with Latent Neural Grammarscited by 30
  12. 2022A Survey of Mix-based Data Augmentation: Taxonomy, Methods, Applications, and Explainabilitycited by 30
38 more
  1. 2025MathFusion: Enhancing Mathematical Problem-solving of LLM through Instruction Fusioncited by 30
  2. 2021Finding needles in a haystack: Sampling Structurally-diverse Training Sets from Synthetic Data for Compositional Generalizationcited by 27
  3. 2023On Efficient Training of Large-Scale Deep Learning Models: A Literature Reviewcited by 26
  4. 2021Substructure Substitution: Structured Data Augmentation for NLPcited by 25
  5. 2022Revisiting the Compositional Generalization Abilities of Neural Sequence Modelscited by 20
  6. 2024Masked Thought: Simply Masking Partial Reasoning Steps Can Improve Mathematical Reasoning Learning of Language Modelscited by 20
  7. 2024A Survey on Mixup Augmentations and Beyondcited by 18
  8. 2022TreeMix: Compositional Constituency-based Data Augmentation for Natural Language Understandingcited by 15
  9. 2021Self-supervised and Supervised Joint Training for Resource-rich Machine Translationcited by 12
  10. 2021To Augment or Not to Augment? A Comparative Study on Text Augmentation Techniques for Low-Resource NLPcited by 12
  11. 2023Generating Data for Symbolic Language with Large Language Modelscited by 12
  12. 2024Retrieval-Augmented Data Augmentation for Low-Resource Domain Taskscited by 12
  13. 2023Model-Agnostic Meta-Learning for Multilingual Hate Speech Detectioncited by 11
  14. 2024Delving into the Reversal Curse: How Far Can Large Language Models Generalize?cited by 11
  15. 2021Mixup Decoding for Diverse Machine Translationcited by 10
  16. 2022Grammar-Based Grounded Lexicon Learningcited by 10
  17. 2022DoubleMix: Simple Interpolation-Based Data Augmentation for Text Classificationcited by 10
  18. 2023GDA: Generative Data Augmentation Techniques for Relation Extraction Taskscited by 10
  19. 2022CipherDAug: Ciphertext based Data Augmentation for Neural Machine Translationcited by 9
  20. 2021Rethinking Data Augmentation for Low-Resource Neural Machine Translation: A Multi-Task Learning Approachcited by 8
  21. 2022Structurally Diverse Sampling for Sample-Efficient Training and Comprehensive Evaluationcited by 8
  22. 2022BLISS: Robust Sequence-to-Sequence Learning via Self-Supervised Input Representationcited by 8
  23. 2022GeoECG: Data Augmentation via Wasserstein Geodesic Perturbation for Robust Electrocardiogram Predictioncited by 8
  24. 2023Learning to Substitute Spans towards Improving Compositional Generalizationcited by 8
  25. 2021Data Augmentation for Mental Health Classification on Social Mediacited by 7
  26. 2022Multilingual Mix: Example Interpolation Improves Multilingual Neural Machine Translationcited by 7
  27. 2023MAGNIFICo: Evaluating the In-Context Learning Ability of Large Language Models to Generalize to Novel Interpretationscited by 7
  28. 2024The Effects of Hallucinations in Synthetic Training Data for Relation Extractioncited by 7
  29. 2024Compositional Generalization Across Distributional Shifts with Sparse Tree Operationscited by 5
  30. 2022AdMix: A Mixed Sample Data Augmentation Method for Neural Machine Translationcited by 4
  31. 2023Weakly Supervised Vision-and-Language Pre-training with Relative Representationscited by 4
  32. 2024IndiText Boost: Text Augmentation for Low Resource India Languagescited by 4
  33. 2024InstaSynth: Opportunities and Challenges in Generating Synthetic Instagram Data with ChatGPT for Sponsored Content Detectioncited by 4
  34. 2021Data Augmentation Methods for Anaphoric Zero Pronounscited by 3
  35. 2023Learning to Compose Representations of Different Encoder Layers towards Improving Compositional Generalizationcited by 3
  36. 2023Understanding Compositional Data Augmentation in Typologically Diverse Morphological Inflectioncited by 3
  37. 2023RoAST: Robustifying Language Models via Adversarial Perturbation with Selective Trainingcited by 3
  38. 2024SFTMix: Elevating Language Model Instruction Tuning with Mixup Recipecited by 3

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