Data matters. Accordingly, data augmentation has been an important ingredient for boosting performances of learned models. Prior data augmentation methods for few-shot text classification have led to great performance boosts. However, they have not been designed to capture the intricate compositional structure of natural language. As a result, they fail to generate samples with plausible and diverse sentence structures. Motivated by this, we present the data augmentation using lexicalized probabilistic context-free grammars that generates augmented samples with diverse syntactic structures with plausible grammar.