Citation

Tan, Siao Wah and Lee, Chin Poo and Lim, Kian Ming and Alqahtani, Ali (2025) QARR-QGF: A Dual-Module Data Augmentation Framework for Enhanced Few-Shot Question Answering. IEEE Access, 13. pp. 160722-160736. ISSN 2169-3536

Text QARR-QGF_ A Dual-Module Data Augmentation Framework for Enhanced Few-Shot Question Answering.pdf - Published Version Restricted to Repository staff only Download (2MB)

Abstract

Few-shot question answering (QA) aims to train effective QA models using very limited annotated data, but existing approaches often struggle to generalize due to insufficient training examples. This paper proposes a two-stage data augmentation framework, called Question-Answer Replacement and Removal and Question Generation and Filtering (QARR-QGF), to address this challenge. The QARR module enhances pretraining data by systematically replacing and removing question-answer pairs to create more diverse examples. During fine-tuning, the QGF module generates paraphrased questions and applies semantic filtering to retain high-quality training samples. The framework is evaluated using three widely used generative models: Longformer-Encoder-Decoder (LED), BART, and T5, on the SQuAD, HotpotQA, and Natural Questions datasets. Experimental results show that QARR-QGF consistently improves performance across all datasets and few-shot settings. For example, the QARR-QGF-T5 model achieves F1 scores of 82.3% on SQuAD, 59.9% on HotpotQA, and 59.0% on Natural Questions in the 16-shot setting, outperforming previous state-of-the-art methods. These results demonstrate the effectiveness of QARR-QGF in improving few-shot QA performance by generating richer and more diverse training data.

Item Type:	Article
Uncontrolled Keywords:	Deep learning
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	30 Sep 2025 08:07
Last Modified:	05 Oct 2025 15:22
URII:	http://shdl.mmu.edu.my/id/eprint/14605

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