Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Span Model for Open Information Extraction on Accurate Corpus   Open information extraction (Open IE) is a challenging task especially due to
its brittle data basis. Most of Open IE systems have to be trained on
automatically built corpus and evaluated on inaccurate test set. In this work,
we first alleviate this difficulty from both sides of training and test sets.
For the former, we propose an improved model design to more sufficiently
exploit training dataset. For the latter, we present our accurately
re-annotated benchmark test set (Re-OIE6) according to a series of linguistic
observation and analysis. Then, we introduce a span model instead of previous
adopted sequence labeling formulization for n-ary Open IE. Our newly introduced
model achieves new state-of-the-art performance on both benchmark evaluation
datasets.

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The categories of this paper are: plasm-ph
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The categories of this paper are: econ
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The categories of this paper are: chem-ph
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The categories of this paper are: solv-int
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The categories of this paper are: acc-phys
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The categories of this paper are: quant-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: astro-ph
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The categories of this paper are: q-fin
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The categories of this paper are: gr-qc
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The categories of this paper are: physics
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The categories of this paper are: adap-org
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The categories of this paper are: q-alg
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The categories of this paper are: math
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The categories of this paper are: ao-sci
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The categories of this paper are: supr-con