CascadeXML: Rethinking Transformers for End-to-end Multi-resolution Training in Extreme Multi-label Classification. (arXiv:2211.00640v1 [cs.LG])

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 approaches, such as XR-Transformer and LightXML, leverage
a transformer instance to achieve state-of-the-art performance. However, in
this process, these approaches need to make various trade-offs between
performance and computational requirements. A major shortcoming, as compared to
the Bi-LSTM based AttentionXML, is that they fail to keep separate feature
representations for each resolution in a label tree. We thus propose
CascadeXML, an end-to-end multi-resolution learning pipeline, which can harness
the multi-layered architecture of a transformer model for attending to
different label resolutions with separate feature representations. CascadeXML
significantly outperforms all existing approaches with non-trivial gains
obtained on benchmark datasets consisting of up to three million labels. Code
for CascadeXML will be made publicly available at



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