Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relations
This addresses data scarcity in text classification, though it is incremental as it builds on existing language model techniques.
The authors tackled the problem of limited labeled data for text classification by proposing contextual augmentation, a method that replaces words with contextually appropriate alternatives predicted by a language model, and showed it improves classifier performance across six tasks.
We propose a novel data augmentation for labeled sentences called contextual augmentation. We assume an invariance that sentences are natural even if the words in the sentences are replaced with other words with paradigmatic relations. We stochastically replace words with other words that are predicted by a bi-directional language model at the word positions. Words predicted according to a context are numerous but appropriate for the augmentation of the original words. Furthermore, we retrofit a language model with a label-conditional architecture, which allows the model to augment sentences without breaking the label-compatibility. Through the experiments for six various different text classification tasks, we demonstrate that the proposed method improves classifiers based on the convolutional or recurrent neural networks.