  Extreme Multi-label text Classification (XMC) is a task of finding the most
relevant labels from a large label set. Nowadays deep learning-based methods
have shown significant success in XMC. However, the existing methods (e.g.,
AttentionXML and X-Transformer etc) still suffer from 1) combining several
models to train and predict for one dataset, and 2) sampling negative labels
statically during the process of training label ranking model, which reduces
both the efficiency and accuracy of the model. To address the above problems,
we proposed LightXML, which adopts end-to-end training and dynamic negative
labels sampling. In LightXML, we use generative cooperative networks to recall
and rank labels, in which label recalling part generates negative and positive
labels, and label ranking part distinguishes positive labels from these labels.
Through these networks, negative labels are sampled dynamically during label
ranking part training by feeding with the same text representation. Extensive
experiments show that LightXML outperforms state-of-the-art methods in five
extreme multi-label datasets with much smaller model size and lower
computational complexity. In particular, on the Amazon dataset with 670K
labels, LightXML can reduce the model size up to 72% compared to AttentionXML.
