Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improving Question Answering by Commonsense-Based Pre-Training   Although neural network approaches achieve remarkable success on a variety of
NLP tasks, many of them struggle to answer questions that require commonsense
knowledge. We believe the main reason is the lack of commonsense
\mbox{connections} between concepts. To remedy this, we provide a simple and
effective method that leverages external commonsense knowledge base such as
ConceptNet. We pre-train direct and indirect relational functions between
concepts, and show that these pre-trained functions could be easily added to
existing neural network models. Results show that incorporating
commonsense-based function improves the baseline on three question answering
tasks that require commonsense reasoning. Further analysis shows that our
system \mbox{discovers} and leverages useful evidence from an external
commonsense knowledge base, which is missing in existing neural network models
and help derive the correct answer.

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The categories of this paper are: nlin
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The categories of this paper are: patt-sol
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The categories of this paper are: solv-int
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The categories of this paper are: atom-ph
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The categories of this paper are: q-fin
2
The categories of this paper are: math
4
The categories of this paper are: cond-mat
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The categories of this paper are: cmp-lg
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The categories of this paper are: nucl-th
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The categories of this paper are: dg-ga
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The categories of this paper are: eess
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The categories of this paper are: q-alg
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The categories of this paper are: comp-gas
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The categories of this paper are: chao-dyn
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The categories of this paper are: quant-ph
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The categories of this paper are: nucl-ex