Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Natural Language to Structured Query Generation via Meta-Learning   In conventional supervised training, a model is trained to fit all the
training examples. However, having a monolithic model may not always be the
best strategy, as examples could vary widely. In this work, we explore a
different learning protocol that treats each example as a unique pseudo-task,
by reducing the original learning problem to a few-shot meta-learning scenario
with the help of a domain-dependent relevance function. When evaluated on the
WikiSQL dataset, our approach leads to faster convergence and achieves
1.1%-5.4% absolute accuracy gains over the non-meta-learning counterparts.

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The categories of this paper are: atom-ph
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The categories of this paper are: econ
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The categories of this paper are: cmp-lg
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The categories of this paper are: q-bio
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The categories of this paper are: alg-geom
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The categories of this paper are: hep-ex
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The categories of this paper are: nucl-th
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The categories of this paper are: dg-ga
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The categories of this paper are: physics
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The categories of this paper are: funct-an
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The categories of this paper are: quant-ph
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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: solv-int
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The categories of this paper are: q-alg
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The categories of this paper are: astro-ph