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

• URL: http://arxiv.org/abs/2402.09141v1
• Date: Wed, 14 Feb 2024 12:41:09 GMT
• Title: Advancing NLP Models with Strategic Text Augmentation: A Comprehensive Study of Augmentation Methods and Curriculum Strategies
• Authors: Himmet Toprak Kesgin, Mehmet Fatih Amasyali
• Abstract summary: This study conducts a thorough evaluation of text augmentation techniques across a variety of datasets and natural language processing (NLP) tasks. 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Scaling Up RL: Unlocking Diverse Reasoning in LLMs via Prolonged Training [121.5858973157225]
  We investigate the effects of prolonged reinforcement learning on a small language model across a diverse set of reasoning domains.<n>We introduce controlled KL regularization, clipping ratio, and periodic reference policy resets as critical components for unlocking long-term performance gains.<n>Our model achieves significant improvements over strong baselines, including +14.7% on math, +13.9% on coding, and +54.8% on logic puzzle tasks.
  arXiv  Detail & Related papers  (2025-07-16T17:59:24Z)
• The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future [5.001689778344014]
  Large Language Models (LLMs) have revolutionized the field of Natural Language Processing (NLP)<n>The idea of prompt engineering and subsequent optimization strategies with LLMs has emerged as a particularly impactful trend to yield a substantial performance boost across diverse NLP tasks.<n>This paper provides unique and comprehensive insights about the potential of diverse prompt optimization strategies.
  arXiv  Detail & Related papers  (2025-06-21T12:25:37Z)
• DSMentor: Enhancing Data Science Agents with Curriculum Learning and Online Knowledge Accumulation [59.79833777420334]
  Large language model (LLM) agents have shown promising performance in generating code for solving complex data science problems.<n>We develop a novel inference-time optimization framework, referred to as DSMentor, to enhance LLM agent performance.<n>Our work underscores the importance of developing effective strategies for accumulating and utilizing knowledge during inference.
  arXiv  Detail & Related papers  (2025-05-20T10:16:21Z)
• DYSTIL: Dynamic Strategy Induction with Large Language Models for Reinforcement Learning [27.336254612018404]
  Reinforcement learning from expert demonstrations has long remained a challenging research problem.<n>Existing state-of-the-art methods using behavioral cloning plus further RL training often suffer from poor generalization, low sample efficiency, and poor model interpretability.<n>We propose a novel strategy-based reinforcement learning framework integrated with large language models (LLMs) to overcome these limitations.
  arXiv  Detail & Related papers  (2025-05-06T05:53:09Z)
• Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining [55.262510814326035]
  Existing reweighting strategies primarily focus on group-level data importance. We introduce novel algorithms for dynamic, instance-level data reweighting. Our framework allows us to devise reweighting strategies deprioritizing redundant or uninformative data.
  arXiv  Detail & Related papers  (2025-02-10T17:57:15Z)
• Improving Academic Skills Assessment with NLP and Ensemble Learning [7.803554057024728]
  This study addresses the critical challenges of assessing foundational academic skills by leveraging advancements in natural language processing (NLP) Our approach integrates multiple state-of-the-art NLP models, including BERT, RoBERTa, BART, DeBERTa, and T5. The methodology involves detailed data preprocessing, feature extraction, and pseudo-label learning to optimize model performance.
  arXiv  Detail & Related papers  (2024-09-23T23:43:43Z)
• Analyzing Persuasive Strategies in Meme Texts: A Fusion of Language Models with Paraphrase Enrichment [0.23020018305241333]
  This paper describes our approach to hierarchical multi-label detection of persuasion techniques in meme texts. The scope of the study encompasses enhancing model performance through innovative training techniques and data augmentation strategies.
  arXiv  Detail & Related papers  (2024-07-01T20:25:20Z)
• Parameter-Efficient Active Learning for Foundational models [7.799711162530711]
  Foundational vision transformer models have shown impressive few shot performance on many vision tasks. This research presents a novel investigation into the application of parameter efficient fine-tuning methods within an active learning (AL) framework.
  arXiv  Detail & Related papers  (2024-06-13T16:30:32Z)
• Improving Forward Compatibility in Class Incremental Learning by Increasing Representation Rank and Feature Richness [3.0620294646308754]
  We introduce an effective-Rank based Feature Richness enhancement (RFR) method, designed for improving forward compatibility. Our results demonstrate the effectiveness of our approach in enhancing novel-task performance while mitigating catastrophic forgetting.
  arXiv  Detail & Related papers  (2024-03-22T11:14:30Z)
• Enhancing Effectiveness and Robustness in a Low-Resource Regime via Decision-Boundary-aware Data Augmentation [16.35126275175784]
  This paper proposes a decision-boundary-aware data augmentation strategy to enhance robustness using pretrained language models. The proposed technique first focuses on shifting the latent features closer to the decision boundary, followed by reconstruction to generate an ambiguous version with a soft label.
  arXiv  Detail & Related papers  (2024-03-22T05:18:08Z)
• Order Matters in the Presence of Dataset Imbalance for Multilingual Learning [53.74649778447903]
  We present a simple yet effective method of pre-training on high-resource tasks, followed by fine-tuning on a mixture of high/low-resource tasks. We show its improvements in neural machine translation (NMT) and multi-lingual language modeling.
  arXiv  Detail & Related papers  (2023-12-11T05:46:57Z)
• The Efficiency Spectrum of Large Language Models: An Algorithmic Survey [54.19942426544731]
  The rapid growth of Large Language Models (LLMs) has been a driving force in transforming various domains. This paper examines the multi-faceted dimensions of efficiency essential for the end-to-end algorithmic development of LLMs.
  arXiv  Detail & Related papers  (2023-12-01T16:00:25Z)
• Training Strategies for Improved Lip-reading [61.661446956793604]
  We investigate the performance of state-of-the-art data augmentation approaches, temporal models and other training strategies. A combination of all the methods results in a classification accuracy of 93.4%, which is an absolute improvement of 4.6% over the current state-of-the-art performance. An error analysis of the various training strategies reveals that the performance improves by increasing the classification accuracy of hard-to-recognise words.
  arXiv  Detail & Related papers  (2022-09-03T09:38:11Z)
• Guiding Generative Language Models for Data Augmentation in Few-Shot Text Classification [59.698811329287174]
  We leverage GPT-2 for generating artificial training instances in order to improve classification performance. Our results show that fine-tuning GPT-2 in a handful of label instances leads to consistent classification improvements.
  arXiv  Detail & Related papers  (2021-11-17T12:10:03Z)
• SDA: Improving Text Generation with Self Data Augmentation [88.24594090105899]
  We propose to improve the standard maximum likelihood estimation (MLE) paradigm by incorporating a self-imitation-learning phase for automatic data augmentation. Unlike most existing sentence-level augmentation strategies, our method is more general and could be easily adapted to any MLE-based training procedure.
  arXiv  Detail & Related papers  (2021-01-02T01:15:57Z)
• Automatic Data Augmentation via Deep Reinforcement Learning for Effective Kidney Tumor Segmentation [57.78765460295249]
  We develop a novel automatic learning-based data augmentation method for medical image segmentation. In our method, we innovatively combine the data augmentation module and the subsequent segmentation module in an end-to-end training manner with a consistent loss. We extensively evaluated our method on CT kidney tumor segmentation which validated the promising results of our method.
  arXiv  Detail & Related papers  (2020-02-22T14:10:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.