Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Modeling question asking using neural program generation   People ask questions that are far richer, more informative, and more creative
than current AI systems. We propose a neuro-symbolic framework for modeling
human question asking, which represents questions as formal programs and
generates programs with an encoder-decoder based deep neural network. From
extensive experiments using an information-search game, we show that our method
can predict which questions humans are likely to ask in unconstrained settings.
We also propose a novel grammar-based question generation framework trained
with reinforcement learning, which is able to generate creative questions
without supervised human data.

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The categories of this paper are: cond-mat
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The categories of this paper are: chem-ph
23
The categories of this paper are: solv-int
15
The categories of this paper are: nucl-ex
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The categories of this paper are: hep-ex
10
The categories of this paper are: q-bio
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The categories of this paper are: quant-ph
24
The categories of this paper are: dg-ga
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The categories of this paper are: comp-gas
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The categories of this paper are: astro-ph
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The categories of this paper are: nlin
31
The categories of this paper are: ao-sci
21
The categories of this paper are: adap-org
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The categories of this paper are: patt-sol
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The categories of this paper are: chao-dyn
28
The categories of this paper are: alg-geom