Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Exploiting Domain Knowledge via Grouped Weight Sharing with Application
  to Text Categorization   A fundamental advantage of neural models for NLP is their ability to learn
representations from scratch. However, in practice this often means ignoring
existing external linguistic resources, e.g., WordNet or domain specific
ontologies such as the Unified Medical Language System (UMLS). We propose a
general, novel method for exploiting such resources via weight sharing. Prior
work on weight sharing in neural networks has considered it largely as a means
of model compression. In contrast, we treat weight sharing as a flexible
mechanism for incorporating prior knowledge into neural models. We show that
this approach consistently yields improved performance on classification tasks
compared to baseline strategies that do not exploit weight sharing.

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The categories of this paper are: plasm-ph
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The categories of this paper are: physics
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The categories of this paper are: chem-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: cond-mat
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The categories of this paper are: econ
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The categories of this paper are: patt-sol
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The categories of this paper are: chao-dyn
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The categories of this paper are: stat
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The categories of this paper are: alg-geom
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The categories of this paper are: acc-phys
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The categories of this paper are: comp-gas
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The categories of this paper are: q-fin
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The categories of this paper are: adap-org
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The categories of this paper are: hep-ex
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The categories of this paper are: dg-ga