Data augmentation is a widely used technique in machine learning to improve model performance. However, existing data augmentation techniques in natural language understanding (NLU) may not fully capture the complexity of natural language variations, and they can be challenging to apply to large datasets. This paper proposes the Random Position Noise (RPN) algorithm, a novel data augmentation technique that operates at the word vector level. RPN modifies the word embeddings of the original text by introducing noise based on the existing values of selected word vectors, allowing for more fine-grained modifications and better capturing natural language variations. Unlike traditional data augmentation methods, RPN does not require gradients in the computational graph during virtual sample updates, making it simpler to apply to large datasets. Experimental results demonstrate that RPN consistently outperforms existing data augmentation techniques across various NLU tasks, including sentiment analysis, natural language inference, and paraphrase detection. Moreover, RPN performs well in low-resource settings and is applicable to any model featuring a word embeddings layer. The proposed RPN algorithm is a promising approach for enhancing NLU performance and addressing the challenges associated with traditional data augmentation techniques in large-scale NLU tasks. Our experimental results demonstrated that the RPN algorithm achieved state-of-the-art performance in all seven NLU tasks, thereby highlighting its effectiveness and potential for real-world NLU applications.

翻译：摘要：数据增强是机器学习中广泛使用的提升模型性能的技术。然而，现有的自然语言理解（NLU）数据增强方法可能无法充分捕捉自然语言变异的复杂性，且难以应用于大规模数据集。本文提出随机位置噪声（RPN）算法，这是一种作用于词向量层面的新型数据增强技术。RPN通过基于所选词向量现有值引入噪声来修改原始文本的词嵌入，能够实现更细粒度的修改并更好地捕捉自然语言变异。与传统数据增强方法不同，RPN在虚拟样本更新过程中无需计算图梯度，使其更易于应用于大规模数据集。实验结果表明，在情感分析、自然语言推理和释义检测等多项NLU任务中，RPN始终优于现有数据增强技术。此外，RPN在低资源场景下表现优异，且可适用于任何包含词嵌入层的模型。所提出的RPN算法是提升NLU性能、应对传统数据增强技术在大规模NLU任务中挑战的有效方案。我们的实验结果显示，RPN算法在所有七项NLU任务中均达到了最优性能，充分验证了其在实际NLU应用中的有效性与潜力。