mlsanityData to 14 Oct 2026

Evaluation Metrics for Text Data Augmentation in NLP

arXiv 2402.06766 ↗First posted 9 Feb 2024cs.CL cs.AIcited by 0

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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.

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References · 23

  1. 2019Unsupervised Data Augmentation for Consistency Trainingcited by 1,060
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11 more
  1. 2021Neural Data-to-Text Generation with LM-based Text Augmentationcited by 22
  2. 2019Improving Neural Machine Translation Robustness via Data Augmentation: Beyond Back Translationcited by 16
  3. 2022TreeMix: Compositional Constituency-based Data Augmentation for Natural Language Understandingcited by 15
  4. 2021ALP: Data Augmentation using Lexicalized PCFGs for Few-Shot Text Classificationcited by 10
  5. 2022DoubleMix: Simple Interpolation-Based Data Augmentation for Text Classificationcited by 10
  6. 2019Data augmentation for low resource sentiment analysis using generative adversarial networkscited by 7
  7. 2022EPiDA: An Easy Plug-in Data Augmentation Framework for High Performance Text Classificationcited by 7
  8. 2022Textual Data Augmentation for Patient Outcomes Predictioncited by 6
  9. 2021Virtual Data Augmentation: A Robust and General Framework for Fine-tuning Pre-trained Modelscited by 5
  10. 2022Fairness for Text Classification Tasks with Identity Information Data Augmentation Methodscited by 4
  11. 2022The use of Data Augmentation as a technique for improving neural network accuracy in detecting fake news about COVID-19cited by 3

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