  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.
