Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning compositional structures for semantic graph parsing   AM dependency parsing is a method for neural semantic graph parsing that
exploits the principle of compositionality. While AM dependency parsers have
been shown to be fast and accurate across several graphbanks, they require
explicit annotations of the compositional tree structures for training. In the
past, these were obtained using complex graphbank-specific heuristics written
by experts. Here we show how they can instead be trained directly on the graphs
with a neural latent-variable model, drastically reducing the amount and
complexity of manual heuristics. We demonstrate that our model picks up on
several linguistic phenomena on its own and achieves comparable accuracy to
supervised training, greatly facilitating the use of AM dependency parsing for
new sembanks.

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The categories of this paper are: acc-phys
28
The categories of this paper are: alg-geom
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The categories of this paper are: chao-dyn
18
The categories of this paper are: econ
15
The categories of this paper are: nucl-ex
36
The categories of this paper are: cmp-lg
2
The categories of this paper are: math
13
The categories of this paper are: quant-ph
26
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: supr-con
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The categories of this paper are: hep-ex
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The categories of this paper are: astro-ph
29
The categories of this paper are: funct-an
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The categories of this paper are: patt-sol
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The categories of this paper are: q-fin