Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Explicit Interaction Model towards Text Classification   Text classification is one of the fundamental tasks in natural language
processing. Recently, deep neural networks have achieved promising performance
in the text classification task compared to shallow models. Despite of the
significance of deep models, they ignore the fine-grained (matching signals
between words and classes) classification clues since their classifications
mainly rely on the text-level representations. To address this problem, we
introduce the interaction mechanism to incorporate word-level matching signals
into the text classification task. In particular, we design a novel framework,
EXplicit interAction Model (dubbed as EXAM), equipped with the interaction
mechanism. We justified the proposed approach on several benchmark datasets
including both multi-label and multi-class text classification tasks. Extensive
experimental results demonstrate the superiority of the proposed method. As a
byproduct, we have released the codes and parameter settings to facilitate
other researches.

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The categories of this paper are: cond-mat
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The categories of this paper are: astro-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: nlin
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The categories of this paper are: chao-dyn
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The categories of this paper are: cmp-lg
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The categories of this paper are: hep-ex
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The categories of this paper are: patt-sol
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The categories of this paper are: gr-qc
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The categories of this paper are: ao-sci
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The categories of this paper are: acc-phys
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The categories of this paper are: bayes-an
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The categories of this paper are: stat
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The categories of this paper are: chem-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: hep-lat