Gandalf : Data Augmentation is all you need for Extreme Classification

Extreme Multi-label Text Classification (XMC) involves learning a classifier that can assign an input with a subset of most relevant labels from millions of label choices. Recent works in this domain have increasingly focused on the problem setting with short-text input data, and labels endowed with short textual descriptions called label features. Short-text XMC with label features has found numerous applications in areas such as prediction of related searches, title-based product recommendation, bid-phrase suggestion, amongst others. In this paper, we propose Gandalf, a graph induced data augmentation based on label features, such that the generated data-points can supplement the training distribution. By exploiting the characteristics of the short-text XMC problem, it leverages the label features to construct valid training instances, and uses the label graph for generating the corresponding soft-label targets, hence effectively capturing the label-label correlations. While most recent advances (such as SiameseXML and ECLARE) in XMC have been algorithmic, mainly aimed towards developing novel deep-learning architectures, our data-centric augmentation approach is orthogonal to these methodologies. We demonstrate the generality and effectiveness of Gandalf by showing up to 30% relative improvements for 5 state-of-the-art algorithms across 4 benchmark datasets consisting of up to 1.3 million labels.