Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Adversarial Training for Commonsense Inference   We propose an AdversariaL training algorithm for commonsense InferenCE
(ALICE). We apply small perturbations to word embeddings and minimize the
resultant adversarial risk to regularize the model. We exploit a novel
combination of two different approaches to estimate these perturbations: 1)
using the true label and 2) using the model prediction. Without relying on any
human-crafted features, knowledge bases, or additional datasets other than the
target datasets, our model boosts the fine-tuning performance of RoBERTa,
achieving competitive results on multiple reading comprehension datasets that
require commonsense inference.

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The categories of this paper are: bayes-an
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The categories of this paper are: nlin
28
The categories of this paper are: alg-geom
0
The categories of this paper are: hep-ph
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The categories of this paper are: patt-sol
6
The categories of this paper are: astro-ph
34
The categories of this paper are: chem-ph
12
The categories of this paper are: nucl-th
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The categories of this paper are: plasm-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: stat
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The categories of this paper are: dg-ga
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The categories of this paper are: cmp-lg
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The categories of this paper are: econ
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The categories of this paper are: acc-phys
30
The categories of this paper are: q-alg