Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: AdvEntuRe: Adversarial Training for Textual Entailment with
  Knowledge-Guided Examples   We consider the problem of learning textual entailment models with limited
supervision (5K-10K training examples), and present two complementary
approaches for it. First, we propose knowledge-guided adversarial example
generators for incorporating large lexical resources in entailment models via
only a handful of rule templates. Second, to make the entailment model - a
discriminator - more robust, we propose the first GAN-style approach for
training it using a natural language example generator that iteratively adjusts
based on the discriminator's performance. We demonstrate effectiveness using
two entailment datasets, where the proposed methods increase accuracy by 4.7%
on SciTail and by 2.8% on a 1% training sub-sample of SNLI. Notably, even a
single hand-written rule, negate, improves the accuracy on the negation
examples in SNLI by 6.1%.

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The categories of this paper are: hep-th
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The categories of this paper are: alg-geom
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The categories of this paper are: acc-phys
5
The categories of this paper are: gr-qc
16
The categories of this paper are: stat
23
The categories of this paper are: solv-int
0
The categories of this paper are: hep-ph
12
The categories of this paper are: nucl-th
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The categories of this paper are: chao-dyn
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The categories of this paper are: q-bio
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The categories of this paper are: bayes-an
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The categories of this paper are: q-alg
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The categories of this paper are: funct-an
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The categories of this paper are: atom-ph
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The categories of this paper are: econ
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The categories of this paper are: nlin