Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: PIQA: Reasoning about Physical Commonsense in Natural Language   To apply eyeshadow without a brush, should I use a cotton swab or a
toothpick? Questions requiring this kind of physical commonsense pose a
challenge to today's natural language understanding systems. While recent
pretrained models (such as BERT) have made progress on question answering over
more abstract domains - such as news articles and encyclopedia entries, where
text is plentiful - in more physical domains, text is inherently limited due to
reporting bias. Can AI systems learn to reliably answer physical common-sense
questions without experiencing the physical world? In this paper, we introduce
the task of physical commonsense reasoning and a corresponding benchmark
dataset Physical Interaction: Question Answering or PIQA. Though humans find
the dataset easy (95% accuracy), large pretrained models struggle (77%). We
provide analysis about the dimensions of knowledge that existing models lack,
which offers significant opportunities for future research.

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The categories of this paper are: solv-int
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The categories of this paper are: quant-ph
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The categories of this paper are: econ
35
The categories of this paper are: mtrl-th
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The categories of this paper are: q-alg
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The categories of this paper are: adap-org
2
The categories of this paper are: math
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The categories of this paper are: eess
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The categories of this paper are: plasm-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: ao-sci
8
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: acc-phys
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The categories of this paper are: q-bio
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The categories of this paper are: astro-ph