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Extreme Multi-label Text Classification (XMC) involves learning a classifierthat can assign an input with a subset of most relevant labels from millions oflabel choices. Recent approaches, such as XR-Transformer and LightXML, leveragea transformer instance to achieve state-of-the-art performance. However, inthis process, these approaches need to make various trade-offs betweenperformance and computational requirements. A major shortcoming, as compared tothe Bi-LSTM based AttentionXML, is that they fail to keep separate featurerepresentations for each resolution in a label tree. We thus proposeCascadeXML, an end-to-end multi-resolution learning pipeline, which can harnessthe multi-layered architecture of a transformer model for attending todifferent label resolutions with separate feature representations. CascadeXMLsignificantly outperforms all existing approaches with non-trivial gainsobtained on benchmark datasets consisting of up to three million labels. Codefor CascadeXML will be made publicly available at\url{https://github.com/xmc-aalto/cascadexml}.

CascadeXML: Rethinking Transformers for End-to-end Multi-resolution Training in Extreme Multi-label Classification