Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Translating Web Search Queries into Natural Language Questions   Users often query a search engine with a specific question in mind and often
these queries are keywords or sub-sentential fragments. For example, if the
users want to know the answer for "What's the capital of USA", they will most
probably query "capital of USA" or "USA capital" or some keyword-based
variation of this. For example, for the user entered query "capital of USA",
the most probable question intent is "What's the capital of USA?". In this
paper, we are proposing a method to generate well-formed natural language
question from a given keyword-based query, which has the same question intent
as the query. Conversion of keyword-based web query into a well-formed question
has lots of applications, with some of them being in search engines, Community
Question Answering (CQA) website and bots communication. We found a synergy
between query-to-question problem with standard machine translation(MT) task.
We have used both Statistical MT (SMT) and Neural MT (NMT) models to generate
the questions from the query. We have observed that MT models perform well in
terms of both automatic and human evaluation.

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The categories of this paper are: cmp-lg
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The categories of this paper are: nucl-ex
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The categories of this paper are: hep-ex
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The categories of this paper are: patt-sol
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The categories of this paper are: comp-gas
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The categories of this paper are: ao-sci
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The categories of this paper are: quant-ph
12
The categories of this paper are: nucl-th
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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: mtrl-th
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The categories of this paper are: nlin
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The categories of this paper are: adap-org
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The categories of this paper are: gr-qc
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The categories of this paper are: q-alg
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The categories of this paper are: q-bio