Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Capturing Greater Context for Question Generation   Automatic question generation can benefit many applications ranging from
dialogue systems to reading comprehension. While questions are often asked with
respect to long documents, there are many challenges with modeling such long
documents. Many existing techniques generate questions by effectively looking
at one sentence at a time, leading to questions that are easy and not
reflective of the human process of question generation. Our goal is to
incorporate interactions across multiple sentences to generate realistic
questions for long documents. In order to link a broad document context to the
target answer, we represent the relevant context via a multi-stage attention
mechanism, which forms the foundation of a sequence to sequence model. We
outperform state-of-the-art methods on question generation on three
question-answering datasets -- SQuAD, MS MARCO and NewsQA.

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The categories of this paper are: gr-qc
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The categories of this paper are: quant-ph
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The categories of this paper are: supr-con
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The categories of this paper are: math
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The categories of this paper are: econ
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The categories of this paper are: dg-ga
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The categories of this paper are: mtrl-th
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The categories of this paper are: adap-org
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The categories of this paper are: chem-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: q-fin
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The categories of this paper are: hep-th
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The categories of this paper are: hep-lat
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The categories of this paper are: cmp-lg
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The categories of this paper are: hep-ex
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The categories of this paper are: q-bio