Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: What's Going On in Neural Constituency Parsers? An Analysis   A number of differences have emerged between modern and classic approaches to
constituency parsing in recent years, with structural components like grammars
and feature-rich lexicons becoming less central while recurrent neural network
representations rise in popularity. The goal of this work is to analyze the
extent to which information provided directly by the model structure in
classical systems is still being captured by neural methods. To this end, we
propose a high-performance neural model (92.08 F1 on PTB) that is
representative of recent work and perform a series of investigative
experiments. We find that our model implicitly learns to encode much of the
same information that was explicitly provided by grammars and lexicons in the
past, indicating that this scaffolding can largely be subsumed by powerful
general-purpose neural machinery.

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The categories of this paper are: chem-ph
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The categories of this paper are: q-bio
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The categories of this paper are: econ
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The categories of this paper are: dg-ga
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The categories of this paper are: funct-an
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The categories of this paper are: mtrl-th
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The categories of this paper are: ao-sci
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The categories of this paper are: bayes-an
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The categories of this paper are: nlin
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The categories of this paper are: cmp-lg
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The categories of this paper are: eess
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The categories of this paper are: hep-lat
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The categories of this paper are: plasm-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: math
0
The categories of this paper are: hep-ph