  In Natural Language Processing (NLP), finding data augmentation techniques
that can produce high-quality human-interpretable examples has always been
challenging. Recently, leveraging kNN such that augmented examples are
retrieved from large repositories of unlabelled sentences has made a step
toward interpretable augmentation. Inspired by this paradigm, we introduce
Minimax-kNN, a sample efficient data augmentation strategy tailored for
Knowledge Distillation (KD). We exploit a semi-supervised approach based on KD
to train a model on augmented data. In contrast to existing kNN augmentation
techniques that blindly incorporate all samples, our method dynamically selects
a subset of augmented samples that maximizes KL-divergence between the teacher
and student models. This step aims to extract the most efficient samples to
ensure our augmented data covers regions in the input space with maximum loss
value. We evaluated our technique on several text classification tasks and
demonstrated that Minimax-kNN consistently outperforms strong baselines. Our
results show that Minimax-kNN requires fewer augmented examples and less
computation to achieve superior performance over the state-of-the-art kNN-based
augmentation techniques.
