Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks   Transfer and multi-task learning have traditionally focused on either a
single source-target pair or very few, similar tasks. Ideally, the linguistic
levels of morphology, syntax and semantics would benefit each other by being
trained in a single model. We introduce a joint many-task model together with a
strategy for successively growing its depth to solve increasingly complex
tasks. Higher layers include shortcut connections to lower-level task
predictions to reflect linguistic hierarchies. We use a simple regularization
term to allow for optimizing all model weights to improve one task's loss
without exhibiting catastrophic interference of the other tasks. Our single
end-to-end model obtains state-of-the-art or competitive results on five
different tasks from tagging, parsing, relatedness, and entailment tasks.

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The categories of this paper are: math
36
The categories of this paper are: cmp-lg
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The categories of this paper are: cond-mat
30
The categories of this paper are: q-alg
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The categories of this paper are: mtrl-th
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The categories of this paper are: nlin
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The categories of this paper are: astro-ph
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The categories of this paper are: adap-org
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The categories of this paper are: ao-sci
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The categories of this paper are: solv-int
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The categories of this paper are: hep-ex
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The categories of this paper are: atom-ph
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The categories of this paper are: hep-ph
29
The categories of this paper are: funct-an
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The categories of this paper are: alg-geom
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The categories of this paper are: quant-ph