Batch Aggregation: An Approach to Enhance Text Classification with Correlated Augmented Data

• URL: http://arxiv.org/abs/2504.05020v1
• Date: Mon, 07 Apr 2025 12:46:07 GMT
• Title: Batch Aggregation: An Approach to Enhance Text Classification with Correlated Augmented Data
• Authors: Charco Hui, Yalu Wen,
• Abstract summary: We propose a novel approach called 'Batch Aggregation' (BAGG)<n>BAGG explicitly models the dependence of text inputs generated through augmentation by incorporating an additional layer that aggregates results from correlated texts.<n>We found that the increase of performance with BAGG is more obvious in domain specific data sets, with accuracy improvements of up to 10-29%.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Natural language processing models often face challenges due to limited labeled data, especially in domain specific areas, e.g., clinical trials. To overcome this, text augmentation techniques are commonly used to increases sample size by transforming the original input data into artificial ones with the label preserved. However, traditional text classification methods ignores the relationship between augmented texts and treats them as independent samples which may introduce classification error. Therefore, we propose a novel approach called 'Batch Aggregation' (BAGG) which explicitly models the dependence of text inputs generated through augmentation by incorporating an additional layer that aggregates results from correlated texts. Through studying multiple benchmark data sets across different domains, we found that BAGG can improve classification accuracy. We also found that the increase of performance with BAGG is more obvious in domain specific data sets, with accuracy improvements of up to 10-29%. Through the analysis of benchmark data, the proposed method addresses limitations of traditional techniques and improves robustness in text classification tasks. Our result demonstrates that BAGG offers more robust results and outperforms traditional approaches when training data is limited.

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