# Evaluation Metrics for Text Data Augmentation in NLP

arXiv: https://arxiv.org/abs/2402.06766 · HTML: https://mlsanity.com/paper/2402.06766/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 63 references that are in the map.
- First posted on arXiv 9 Feb 2024.

## Builds on (most cited first, 23 of 23)

- [Unsupervised Data Augmentation for Consistency Training](https://mlsanity.com/paper/1904.12848/) (2019)
- [EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks](https://mlsanity.com/paper/1901.11196/) (2019)
- [A Survey of Data Augmentation Approaches for NLP](https://mlsanity.com/paper/2105.03075/) (2021)
- [MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification](https://mlsanity.com/paper/2004.12239/) (2020)
- [Data Augmentation using Pre-trained Transformer Models](https://mlsanity.com/paper/2003.02245/) (2020)
- [Augmented SBERT: Data Augmentation Method for Improving Bi-Encoders for Pairwise Sentence Scoring Tasks](https://mlsanity.com/paper/2010.08240/) (2020)
- [A Survey on Data Augmentation for Text Classification](https://mlsanity.com/paper/2107.03158/) (2021)
- [Conditional BERT Contextual Augmentation](https://mlsanity.com/paper/1812.06705/) (2018)
- [Data Augmentation Approaches in Natural Language Processing: A Survey](https://mlsanity.com/paper/2110.01852/) (2021)
- [Data Boost: Text Data Augmentation Through Reinforcement Learning Guided Conditional Generation](https://mlsanity.com/paper/2012.02952/) (2020)
- [GenAug: Data Augmentation for Finetuning Text Generators](https://mlsanity.com/paper/2010.01794/) (2020)
- [Improving short text classification through global augmentation methods](https://mlsanity.com/paper/1907.03752/) (2019)
- [Neural Data-to-Text Generation with LM-based Text Augmentation](https://mlsanity.com/paper/2102.03556/) (2021)
- [Improving Neural Machine Translation Robustness via Data Augmentation: Beyond Back Translation](https://mlsanity.com/paper/1910.03009/) (2019)
- [TreeMix: Compositional Constituency-based Data Augmentation for Natural Language Understanding](https://mlsanity.com/paper/2205.06153/) (2022)
- [ALP: Data Augmentation using Lexicalized PCFGs for Few-Shot Text Classification](https://mlsanity.com/paper/2112.11916/) (2021)
- [DoubleMix: Simple Interpolation-Based Data Augmentation for Text Classification](https://mlsanity.com/paper/2209.05297/) (2022)
- [Data augmentation for low resource sentiment analysis using generative adversarial networks](https://mlsanity.com/paper/1902.06818/) (2019)
- [EPiDA: An Easy Plug-in Data Augmentation Framework for High Performance Text Classification](https://mlsanity.com/paper/2204.11205/) (2022)
- [Textual Data Augmentation for Patient Outcomes Prediction](https://mlsanity.com/paper/2211.06778/) (2022)
- [Virtual Data Augmentation: A Robust and General Framework for Fine-tuning Pre-trained Models](https://mlsanity.com/paper/2109.05793/) (2021)
- [Fairness for Text Classification Tasks with Identity Information Data Augmentation Methods](https://mlsanity.com/paper/2203.03541/) (2022)
- [The use of Data Augmentation as a technique for improving neural network accuracy in detecting fake news about COVID-19](https://mlsanity.com/paper/2205.00452/) (2022)

## Abstract (arXiv)

Recent surveys on data augmentation for natural language processing have reported different techniques and advancements in the field. Several frameworks, tools, and repositories promote the implementation of text data augmentation pipelines. However, a lack of evaluation criteria and standards for method comparison due to different tasks, metrics, datasets, architectures, and experimental settings makes comparisons meaningless. Also, a lack of methods unification exists and text data augmentation research would benefit from unified metrics to compare different augmentation methods. Thus, academics and the industry endeavor relevant evaluation metrics for text data augmentation techniques. The contribution of this work is to provide a taxonomy of evaluation metrics for text augmentation methods and serve as a direction for a unified benchmark. The proposed taxonomy organizes categories that include tools for implementation and metrics calculation. Finally, with this study, we intend to present opportunities to explore the unification and standardization of text data augmentation metrics.