通过聚合相关增强文本提升小样本文本分类准确率
Batch Aggregation: An Approach to Enhance Text Classification with Correlated Augmented Data
- 引入批量聚合层，显式建模增强文本间的依赖关系
- 在特定领域数据集上最高提升29%分类准确率
- 特别适合标注数据稀缺的垂直领域应用
自然语言处理模型常因标注数据有限而面临挑战，尤其在临床试验等专业领域。为缓解此问题，文本增强技术通过变换原始数据生成带标签的伪样本以扩充数据量。然而，传统分类方法忽视增强文本间的关联性，将其视为独立样本，可能引入分类误差。为此，本文提出新型方法Batch Aggregation（BAGG），通过额外的聚合层显式建模增强文本间的依赖关系。在多个跨领域的基准数据集上验证表明，BAGG可提升分类准确率，尤其在特定领域数据集上效果更显著，准确率提升可达10%-29%。分析显示，该方法有效克服了传统技术的局限性，增强了文本分类任务的鲁棒性。结果表明，在训练数据受限时，BAGG能提供更稳定且优越的性能。
原文摘要 · Abstract (English)
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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