Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Second-Order Neural Dependency Parsing with Message Passing and
  End-to-End Training   In this paper, we propose second-order graph-based neural dependency parsing
using message passing and end-to-end neural networks. We empirically show that
our approaches match the accuracy of very recent state-of-the-art second-order
graph-based neural dependency parsers and have significantly faster speed in
both training and testing. We also empirically show the advantage of
second-order parsing over first-order parsing and observe that the usefulness
of the head-selection structured constraint vanishes when using BERT embedding.

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The categories of this paper are: stat
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The categories of this paper are: ao-sci
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The categories of this paper are: q-fin
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The categories of this paper are: nucl-ex
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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: eess
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The categories of this paper are: hep-lat
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The categories of this paper are: math
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The categories of this paper are: funct-an
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The categories of this paper are: econ
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The categories of this paper are: alg-geom
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The categories of this paper are: gr-qc
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The categories of this paper are: comp-gas
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The categories of this paper are: solv-int
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The categories of this paper are: physics