Not Enough Data? Deep Learning to the Rescue!
The authors use a powerful pre-trained #NLP NN model to artificially synthesize new #labeled #data for #supervised learning. They mainly focus on cases with scarce labeled data. Their method referred to as language-model-based data augmentation (#LAMBADA), involves fine-tuning a SOTA language generator to a specific task through an initial training phase on the existing (usually small) labeled data.
Using the fine-tuned model and given a class label, new sentences for the class are generated. Then they filter these new sentences by using a classifier trained on the original data.
In a series of experiments, they show that LAMBADA improves classifiers performance on a variety of datasets. Moreover, LAMBADA significantly improves upon the SOTA techniques for data augmentation, specifically those applicable to text classification tasks with little data.
paper: https://arxiv.org/abs/1911.03118
The authors use a powerful pre-trained #NLP NN model to artificially synthesize new #labeled #data for #supervised learning. They mainly focus on cases with scarce labeled data. Their method referred to as language-model-based data augmentation (#LAMBADA), involves fine-tuning a SOTA language generator to a specific task through an initial training phase on the existing (usually small) labeled data.
Using the fine-tuned model and given a class label, new sentences for the class are generated. Then they filter these new sentences by using a classifier trained on the original data.
In a series of experiments, they show that LAMBADA improves classifiers performance on a variety of datasets. Moreover, LAMBADA significantly improves upon the SOTA techniques for data augmentation, specifically those applicable to text classification tasks with little data.
paper: https://arxiv.org/abs/1911.03118
