Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Advancing NLP Models with Strategic Text Augmentation: A Comprehensive Study of Augmentation Methods and Curriculum Strategies

Himmet Toprak Kesgin, Mehmet Fatih Amasyali

TL;DR

The paper tackles the lack of generalized evidence for text augmentation in NLP by systematically evaluating a wide range of augmentation methods across multiple tasks and datasets, and by introducing Modified Cyclical Curriculum Learning (MCCL) for augmented data. It deploys BERT-based representations and a diverse augmentation suite, analyzed under rigorous training protocols, including varying augmentation rates and sequencing strategies. Findings show no universal best augmentation technique; however, MCCL combined with augmentation yields notable improvements, while filtering and higher augmentation rates yield nuanced, dataset-dependent effects. The work offers practical guidance on selecting augmentation methods and sequencing strategies, and points to future directions in online augmentation and cross-task applicability.

Abstract

This study conducts a thorough evaluation of text augmentation techniques across a variety of datasets and natural language processing (NLP) tasks to address the lack of reliable, generalized evidence for these methods. It examines the effectiveness of these techniques in augmenting training sets to improve performance in tasks such as topic classification, sentiment analysis, and offensive language detection. The research emphasizes not only the augmentation methods, but also the strategic order in which real and augmented instances are introduced during training. A major contribution is the development and evaluation of Modified Cyclical Curriculum Learning (MCCL) for augmented datasets, which represents a novel approach in the field. Results show that specific augmentation methods, especially when integrated with MCCL, significantly outperform traditional training approaches in NLP model performance. These results underscore the need for careful selection of augmentation techniques and sequencing strategies to optimize the balance between speed and quality improvement in various NLP tasks. The study concludes that the use of augmentation methods, especially in conjunction with MCCL, leads to improved results in various classification tasks, providing a foundation for future advances in text augmentation strategies in NLP.

Advancing NLP Models with Strategic Text Augmentation: A Comprehensive Study of Augmentation Methods and Curriculum Strategies

TL;DR

The paper tackles the lack of generalized evidence for text augmentation in NLP by systematically evaluating a wide range of augmentation methods across multiple tasks and datasets, and by introducing Modified Cyclical Curriculum Learning (MCCL) for augmented data. It deploys BERT-based representations and a diverse augmentation suite, analyzed under rigorous training protocols, including varying augmentation rates and sequencing strategies. Findings show no universal best augmentation technique; however, MCCL combined with augmentation yields notable improvements, while filtering and higher augmentation rates yield nuanced, dataset-dependent effects. The work offers practical guidance on selecting augmentation methods and sequencing strategies, and points to future directions in online augmentation and cross-task applicability.

Abstract

This study conducts a thorough evaluation of text augmentation techniques across a variety of datasets and natural language processing (NLP) tasks to address the lack of reliable, generalized evidence for these methods. It examines the effectiveness of these techniques in augmenting training sets to improve performance in tasks such as topic classification, sentiment analysis, and offensive language detection. The research emphasizes not only the augmentation methods, but also the strategic order in which real and augmented instances are introduced during training. A major contribution is the development and evaluation of Modified Cyclical Curriculum Learning (MCCL) for augmented datasets, which represents a novel approach in the field. Results show that specific augmentation methods, especially when integrated with MCCL, significantly outperform traditional training approaches in NLP model performance. These results underscore the need for careful selection of augmentation techniques and sequencing strategies to optimize the balance between speed and quality improvement in various NLP tasks. The study concludes that the use of augmentation methods, especially in conjunction with MCCL, leads to improved results in various classification tasks, providing a foundation for future advances in text augmentation strategies in NLP.
Paper Structure (19 sections, 1 figure, 7 tables, 1 algorithm)

This paper contains 19 sections, 1 figure, 7 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Literature
  3. Method
  4. Dataset Selection Criteria
  5. Text Representation and Modeling Techniques
  6. Text Augmentation Methods
  7. Experimental Setup and Evaluation Metrics
  8. Experiments
  9. Evaluation of Text Augmentation Methods
  10. Impact of Filtering on Augmented Data
  11. Effect of Different Training Sequences on Model Performance
  12. Comparative Analysis of Augmentation Methods Execution Time
  13. Conclusion
  14. Effectiveness of Specific Augmentation Methods
  15. Impact of Augmentation Rate
  16. ...and 4 more sections

Figures (1)

  • Figure 1: Heatmap Indicating Relative Performance for Each Dataset and Algorithm-Augmentation Pair