  Extreme multi-label text classification (XMTC) refers to the problem of
tagging a given text with the most relevant subset of labels from a large label
set. A majority of labels only have a few training instances due to large label
dimensionality in XMTC. To solve this data sparsity issue, most existing XMTC
methods take advantage of fixed label clusters obtained in early stage to
balance performance on tail labels and head labels. However, such label
clusters provide static and coarse-grained semantic scope for every text, which
ignores distinct characteristics of different texts and has difficulties
modelling accurate semantics scope for texts with tail labels. In this paper,
we propose a novel framework TReaderXML for XMTC, which adopts dynamic and
fine-grained semantic scope from teacher knowledge for individual text to
optimize text conditional prior category semantic ranges. TReaderXML
dynamically obtains teacher knowledge for each text by similar texts and
hierarchical label information in training sets to release the ability of
distinctly fine-grained label-oriented semantic scope. Then, TReaderXML
benefits from a novel dual cooperative network that firstly learns features of
a text and its corresponding label-oriented semantic scope by parallel Encoding
Module and Reading Module, secondly embeds two parts by Interaction Module to
regularize the text's representation by dynamic and fine-grained label-oriented
semantic scope, and finally find target labels by Prediction Module.
Experimental results on three XMTC benchmark datasets show that our method
achieves new state-of-the-art results and especially performs well for severely
imbalanced and sparse datasets.
