Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Denoising Distant Supervision for Relation Extraction via Instance-Level
  Adversarial Training   Existing neural relation extraction (NRE) models rely on distant supervision
and suffer from wrong labeling problems. In this paper, we propose a novel
adversarial training mechanism over instances for relation extraction to
alleviate the noise issue. As compared with previous denoising methods, our
proposed method can better discriminate those informative instances from noisy
ones. Our method is also efficient and flexible to be applied to various NRE
architectures. As shown in the experiments on a large-scale benchmark dataset
in relation extraction, our denoising method can effectively filter out noisy
instances and achieve significant improvements as compared with the
state-of-the-art models.

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The categories of this paper are: gr-qc
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The categories of this paper are: solv-int
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The categories of this paper are: q-alg
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The categories of this paper are: physics
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The categories of this paper are: hep-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: nucl-ex
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The categories of this paper are: econ
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The categories of this paper are: nucl-th
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The categories of this paper are: acc-phys
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The categories of this paper are: astro-ph
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The categories of this paper are: nlin
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The categories of this paper are: eess
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The categories of this paper are: dg-ga
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The categories of this paper are: plasm-ph
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The categories of this paper are: hep-th