Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Simple Global Neural Discourse Parser   Discourse parsing is largely dominated by greedy parsers with
manually-designed features, while global parsing is rare due to its
computational expense. In this paper, we propose a simple chart-based neural
discourse parser that does not require any manually-crafted features and is
based on learned span representations only. To overcome the computational
challenge, we propose an independence assumption between the label assigned to
a node in the tree and the splitting point that separates its children, which
results in tractable decoding. We empirically demonstrate that our model
achieves the best performance among global parsers, and comparable performance
to state-of-art greedy parsers, using only learned span representations.

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The categories of this paper are: cmp-lg
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The categories of this paper are: q-fin
23
The categories of this paper are: solv-int
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The categories of this paper are: math-ph
13
The categories of this paper are: quant-ph
16
The categories of this paper are: stat
15
The categories of this paper are: nucl-ex
21
The categories of this paper are: adap-org
20
The categories of this paper are: acc-phys
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The categories of this paper are: mtrl-th
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The categories of this paper are: atom-ph
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The categories of this paper are: ao-sci
3
The categories of this paper are: physics
11
The categories of this paper are: hep-lat
24
The categories of this paper are: dg-ga
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The categories of this paper are: supr-con