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.
Abstract (translated)
自然语言处理模型常因标注数据有限而在特定领域（如临床试验）中面临挑战。为解决这一问题，通常采用文本增强技术来通过转换原始输入数据生成人工样本以增加样本量，并保留原有标签信息。然而，传统文本分类方法忽略了这些增强文本之间的关系，将其视为独立的样本，这可能会引入分类错误。为此，我们提出了一种名为“批量聚合”（Batch Aggregation，简称BAGG）的新方法，该方法通过添加一层来明确建模通过增强生成的文本输入间的关系，这一层用于聚集相关文本的结果。 通过对不同领域内的多个基准数据集的研究，我们发现BAGG可以提高分类准确率。此外，在特定领域的数据集中，使用BAGG性能提升更为显著，最高可达到10-29%的精度改善。通过分析基准数据，所提出的方法解决了传统技术的局限性，并增强了文本分类任务中的鲁棒性。 我们的结果显示，在训练数据有限的情况下，BAGG方法提供更稳健的结果并优于传统的做法。
URL
https://arxiv.org/abs/2504.05020
