# Text Data Augmentation Made Simple By Leveraging NLP Cloud APIs

arXiv: https://arxiv.org/abs/1812.04718 · HTML: https://mlsanity.com/paper/1812.04718/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Dec 2018.

## Built on by (most cited first, 30 of 40)

- [AugGPT: Leveraging ChatGPT for Text Data Augmentation](https://mlsanity.com/paper/2302.13007/) (2023)
- [A Survey on Data Augmentation for Text Classification](https://mlsanity.com/paper/2107.03158/) (2021)
- [Data Augmentation Approaches in Natural Language Processing: A Survey](https://mlsanity.com/paper/2110.01852/) (2021)
- [LLM2LLM: Boosting LLMs with Novel Iterative Data Enhancement](https://mlsanity.com/paper/2403.15042/) (2024)
- [MixGen: A New Multi-Modal Data Augmentation](https://mlsanity.com/paper/2206.08358/) (2022)
- [PCL: Peer-Contrastive Learning with Diverse Augmentations for Unsupervised Sentence Embeddings](https://mlsanity.com/paper/2201.12093/) (2022)
- [Shifting AI Efficiency From Model-Centric to Data-Centric Compression](https://mlsanity.com/paper/2505.19147/) (2025)
- [Data Augmentation in Natural Language Processing: A Novel Text Generation Approach for Long and Short Text Classifiers](https://mlsanity.com/paper/2103.14453/) (2021)
- [Empowering Large Language Models for Textual Data Augmentation](https://mlsanity.com/paper/2404.17642/) (2024)
- [LearnDA: Learnable Knowledge-Guided Data Augmentation for Event Causality Identification](https://mlsanity.com/paper/2106.01649/) (2021)
- [Connecting Language and Vision for Natural Language-Based Vehicle Retrieval](https://mlsanity.com/paper/2105.14897/) (2021)
- [AugLiChem: Data Augmentation Library of Chemical Structures for Machine Learning](https://mlsanity.com/paper/2111.15112/) (2021)
- [PSSAT: A Perturbed Semantic Structure Awareness Transferring Method for Perturbation-Robust Slot Filling](https://mlsanity.com/paper/2208.11508/) (2022)
- [Rephrasing natural text data with different languages and quality levels for Large Language Model pre-training](https://mlsanity.com/paper/2410.20796/) (2024)
- [Influence-guided Data Augmentation for Neural Tensor Completion](https://mlsanity.com/paper/2108.10248/) (2021)
- [EPiDA: An Easy Plug-in Data Augmentation Framework for High Performance Text Classification](https://mlsanity.com/paper/2204.11205/) (2022)
- [miCSE: Mutual Information Contrastive Learning for Low-shot Sentence Embeddings](https://mlsanity.com/paper/2211.04928/) (2022)
- [Modeling Adversarial Attack on Pre-trained Language Models as Sequential Decision Making](https://mlsanity.com/paper/2305.17440/) (2023)
- [From Big to Small Without Losing It All: Text Augmentation with ChatGPT for Efficient Sentiment Analysis](https://mlsanity.com/paper/2312.04720/) (2023)
- [Towards Robust and Generalizable Training: An Empirical Study of Noisy Slot Filling for Input Perturbations](https://mlsanity.com/paper/2310.03518/) (2023)
- [Iterative Mask Filling: An Effective Text Augmentation Method Using Masked Language Modeling](https://mlsanity.com/paper/2401.01830/) (2024)
- [InstaSynth: Opportunities and Challenges in Generating Synthetic Instagram Data with ChatGPT for Sponsored Content Detection](https://mlsanity.com/paper/2403.15214/) (2024)
- [Improving Performance of Automated Essay Scoring by using back-translation essays and adjusted scores](https://mlsanity.com/paper/2203.00354/) (2022)
- [Global Mixup: Eliminating Ambiguity with Clustering](https://mlsanity.com/paper/2206.02734/) (2022)
- [Multimodal Audio-textual Architecture for Robust Spoken Language Understanding](https://mlsanity.com/paper/2306.06819/) (2023)
- [Textual Data Distributions: Kullback Leibler Textual Distributions Contrasts on GPT-2 Generated Texts, with Supervised, Unsupervised Learning on Vaccine & Market Topics & Sentiment](https://mlsanity.com/paper/2107.02025/) (2021)
- [Mitigating Data Scarcity for Large Language Models](https://mlsanity.com/paper/2302.01806/) (2023)
- [Data Augmentation for Neural NLP](https://mlsanity.com/paper/2302.11412/) (2023)
- [On Evaluation Protocols for Data Augmentation in a Limited Data Scenario](https://mlsanity.com/paper/2402.14895/) (2024)
- [TF-Attack: Transferable and Fast Adversarial Attacks on Large Language Models](https://mlsanity.com/paper/2408.13985/) (2024)

## Abstract (arXiv)

In practice, it is common to find oneself with far too little text data to train a deep neural network. This "Big Data Wall" represents a challenge for minority language communities on the Internet, organizations, laboratories and companies that compete the GAFAM (Google, Amazon, Facebook, Apple, Microsoft). While most of the research effort in text data augmentation aims on the long-term goal of finding end-to-end learning solutions, which is equivalent to "using neural networks to feed neural networks", this engineering work focuses on the use of practical, robust, scalable and easy-to-implement data augmentation pre-processing techniques similar to those that are successful in computer vision. Several text augmentation techniques have been experimented. Some existing ones have been tested for comparison purposes such as noise injection or the use of regular expressions. Others are modified or improved techniques like lexical replacement. Finally more innovative ones, such as the generation of paraphrases using back-translation or by the transformation of syntactic trees, are based on robust, scalable, and easy-to-use NLP Cloud APIs. All the text augmentation techniques studied, with an amplification factor of only 5, increased the accuracy of the results in a range of 4.3% to 21.6%, with significant statistical fluctuations, on a standardized task of text polarity prediction. Some standard deep neural network architectures were tested: the multilayer perceptron (MLP), the long short-term memory recurrent network (LSTM) and the bidirectional LSTM (biLSTM). Classical XGBoost algorithm has been tested with up to 2.5% improvements.