Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Tale of a Probe and a Parser   Measuring what linguistic information is encoded in neural models of language
has become popular in NLP. Researchers approach this enterprise by training
"probes" - supervised models designed to extract linguistic structure from
another model's output. One such probe is the structural probe (Hewitt and
Manning, 2019), designed to quantify the extent to which syntactic information
is encoded in contextualised word representations. The structural probe has a
novel design, unattested in the parsing literature, the precise benefit of
which is not immediately obvious. To explore whether syntactic probes would do
better to make use of existing techniques, we compare the structural probe to a
more traditional parser with an identical lightweight parameterisation. The
parser outperforms structural probe on UUAS in seven of nine analysed
languages, often by a substantial amount (e.g. by 11.1 points in English).
Under a second less common metric, however, there is the opposite trend - the
structural probe outperforms the parser. This begs the question: which metric
should we prefer?

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The categories of this paper are: nucl-th
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The categories of this paper are: bayes-an
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The categories of this paper are: math-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: chao-dyn
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The categories of this paper are: ao-sci
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The categories of this paper are: hep-th
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The categories of this paper are: q-fin
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The categories of this paper are: physics
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The categories of this paper are: nlin
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The categories of this paper are: dg-ga
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The categories of this paper are: astro-ph
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The categories of this paper are: cmp-lg
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The categories of this paper are: math
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The categories of this paper are: adap-org
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The categories of this paper are: supr-con