Not Enough Data? Deep Learning to the Rescue!

Understand

Based on recent advances in natural language modeling and those in text generation capabilities, we propose a novel data augmentation method for text classification tasks.

• We use a powerful pre-trained neural network model to artificially synthesize new labeled data for supervised learning.
• We mainly focus on cases with scarce labeled data.
• Our method, referred to as language-model-based data augmentation (LAMBADA), involves fine-tuning a state-of-the-art language generator to a specific task through an initial training phase on the existing (usually small) labeled data.