Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Incorporating Hierarchy into Text Encoder: a Contrastive Learning
  Approach for Hierarchical Text Classification   Hierarchical text classification is a challenging subtask of multi-label
classification due to its complex label hierarchy. Existing methods encode text
and label hierarchy separately and mix their representations for
classification, where the hierarchy remains unchanged for all input text.
Instead of modeling them separately, in this work, we propose Hierarchy-guided
Contrastive Learning (HGCLR) to directly embed the hierarchy into a text
encoder. During training, HGCLR constructs positive samples for input text
under the guidance of the label hierarchy. By pulling together the input text
and its positive sample, the text encoder can learn to generate the
hierarchy-aware text representation independently. Therefore, after training,
the HGCLR enhanced text encoder can dispense with the redundant hierarchy.
Extensive experiments on three benchmark datasets verify the effectiveness of
HGCLR.

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The categories of this paper are: solv-int
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The categories of this paper are: chem-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: patt-sol
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The categories of this paper are: acc-phys
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The categories of this paper are: q-bio
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The categories of this paper are: cond-mat
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The categories of this paper are: ao-sci
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The categories of this paper are: q-fin
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The categories of this paper are: alg-geom
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The categories of this paper are: comp-gas
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The categories of this paper are: plasm-ph
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The categories of this paper are: eess
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The categories of this paper are: gr-qc
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The categories of this paper are: mtrl-th
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The categories of this paper are: nlin