mlsanityData to 14 Oct 2026

Data Augmentation for Text Generation Without Any Augmented Data

arXiv 2105.13650 ↗First posted 28 May 2021cs.CL cs.AIcited by 4

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Abstract (arXiv)

Data augmentation is an effective way to improve the performance of many neural text generation models. However, current data augmentation methods need to define or choose proper data mapping functions that map the original samples into the augmented samples. In this work, we derive an objective to formulate the problem of data augmentation on text generation tasks without any use of augmented data constructed by specific mapping functions. Our proposed objective can be efficiently optimized and applied to popular loss functions on text generation tasks with a convergence rate guarantee. Experiments on five datasets of two text generation tasks show that our approach can approximate or even surpass popular data augmentation methods.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 28 of its 40 references that are in the map. First posted on arXiv 28 May 2021.

Builds on

28 of 40 references.

References · 28

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  4. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,548
  5. 2015Character-level Convolutional Networks for Text Classificationcited by 2,330
  6. 2019fairseq: A Fast, Extensible Toolkit for Sequence Modelingcited by 1,488
  7. 2015A Diversity-Promoting Objective Function for Neural Conversation Modelscited by 1,077
  8. 2015Effective Approaches to Attention-based Neural Machine Translationcited by 1,026
  9. 2019On the Convergence of Adam and Beyondcited by 841
  10. 2019EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Taskscited by 807
  11. 2015Improving Neural Machine Translation Models with Monolingual Datacited by 788
  12. 2017MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labelscited by 653
16 more
  1. 2019MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distancecited by 418
  2. 2017The Effectiveness of Data Augmentation in Image Classification using Deep Learningcited by 370
  3. 2017Toward Controlled Generation of Textcited by 272
  4. 2018Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relationscited by 201
  5. 2018On the Convergence of A Class of Adam-Type Algorithms for Non-Convex Optimizationcited by 174
  6. 2016Stochastic Variance Reduction for Nonconvex Optimizationcited by 145
  7. 2019Augmenting Data with Mixup for Sentence Classification: An Empirical Studycited by 125
  8. 2018Conditional BERT Contextual Augmentationcited by 107
  9. 2019Robust Neural Machine Translation with Doubly Adversarial Inputscited by 106
  10. 2016Reward Augmented Maximum Likelihood for Neural Structured Predictioncited by 81
  11. 2017Data Noising as Smoothing in Neural Network Language Modelscited by 78
  12. 2019Learning Data Manipulation for Augmentation and Weightingcited by 45
  13. 2020Data Manipulation: Towards Effective Instance Learning for Neural Dialogue Generation via Learning to Augment and Reweightcited by 34
  14. 2019Improving Sequence-to-Sequence Learning via Optimal Transportcited by 33
  15. 2019Automatically Learning Data Augmentation Policies for Dialogue Taskscited by 20
  16. 2020Dialogue Distillation: Open-Domain Dialogue Augmentation Using Unpaired Datacited by 20

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Cited by year

Citing papers per year: 2022: 1; 2023: 1; 2024: 1; 2025: 1.

4 papers.

  1. 2024Best Practices and Lessons Learned on Synthetic Datacited by 106
  2. 2025Enhancing LLM Safety Through a Theoretical Minimax Game Lenscited by 19
  3. 2023On Synthetic Data for Back Translationcited by 4
  4. 2022BootAug: Boosting Text Augmentation via Hybrid Instance Filtering Frameworkcited by 1