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Extreme multi-label text classification (XMC) seeks to find relevant labelsfrom an extreme large label collection for a given text input. Many real-worldapplications can be formulated as XMC problems, such as recommendation systems,document tagging and semantic search. Recently, transformer based XMC methods,such as X-Transformer and LightXML, have shown significant improvement overother XMC methods. Despite leveraging pre-trained transformer models for textrepresentation, the fine-tuning procedure of transformer models on large labelspace still has lengthy computational time even with powerful GPUs. In thispaper, we propose a novel recursive approach, XR-Transformer to accelerate theprocedure through recursively fine-tuning transformer models on a series ofmulti-resolution objectives related to the original XMC objective function.Empirical results show that XR-Transformer takes significantly less trainingtime compared to other transformer-based XMC models while yielding betterstate-of-the-art results. In particular, on the public Amazon-3M dataset with 3million labels, XR-Transformer is not only 20x faster than X-Transformer butalso improves the Precision@1 from 51% to 54%.

Fast Multi-Resolution Transformer Fine-tuning for Extreme Multi-label Text Classification