Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Label-Wise Document Pre-Training for Multi-Label Text Classification   A major challenge of multi-label text classification (MLTC) is to
stimulatingly exploit possible label differences and label correlations. In
this paper, we tackle this challenge by developing Label-Wise Pre-Training
(LW-PT) method to get a document representation with label-aware information.
The basic idea is that, a multi-label document can be represented as a
combination of multiple label-wise representations, and that, correlated labels
always cooccur in the same or similar documents. LW-PT implements this idea by
constructing label-wise document classification tasks and trains label-wise
document encoders. Finally, the pre-trained label-wise encoder is fine-tuned
with the downstream MLTC task. Extensive experimental results validate that the
proposed method has significant advantages over the previous state-of-the-art
models and is able to discover reasonable label relationship. The code is
released to facilitate other researchers.

21
The categories of this paper are: adap-org
29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con
34
The categories of this paper are: chem-ph
10
The categories of this paper are: q-bio
7
The categories of this paper are: hep-th
30
The categories of this paper are: q-alg
35
The categories of this paper are: mtrl-th
15
The categories of this paper are: nucl-ex
23
The categories of this paper are: solv-int
2
The categories of this paper are: math
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The categories of this paper are: eess
22
The categories of this paper are: chao-dyn
5
The categories of this paper are: gr-qc
6
The categories of this paper are: astro-ph
27
The categories of this paper are: comp-gas