Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: KG^2: Learning to Reason Science Exam Questions with Contextual
  Knowledge Graph Embeddings   The AI2 Reasoning Challenge (ARC), a new benchmark dataset for question
answering (QA) has been recently released. ARC only contains natural science
questions authored for human exams, which are hard to answer and require
advanced logic reasoning. On the ARC Challenge Set, existing state-of-the-art
QA systems fail to significantly outperform random baseline, reflecting the
difficult nature of this task. In this paper, we propose a novel framework for
answering science exam questions, which mimics human solving process in an
open-book exam. To address the reasoning challenge, we construct contextual
knowledge graphs respectively for the question itself and supporting sentences.
Our model learns to reason with neural embeddings of both knowledge graphs.
Experiments on the ARC Challenge Set show that our model outperforms the
previous state-of-the-art QA systems.

6
The categories of this paper are: astro-ph
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The categories of this paper are: hep-th
27
The categories of this paper are: comp-gas
29
The categories of this paper are: funct-an
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The categories of this paper are: solv-int
12
The categories of this paper are: nucl-th
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The categories of this paper are: nucl-ex
0
The categories of this paper are: hep-ph
28
The categories of this paper are: alg-geom
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The categories of this paper are: math
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The categories of this paper are: quant-ph
25
The categories of this paper are: patt-sol
31
The categories of this paper are: ao-sci
33
The categories of this paper are: plasm-ph
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The categories of this paper are: q-fin
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The categories of this paper are: nlin