Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: End-to-End Learning for Answering Structured Queries Directly over Text   Structured queries expressed in languages (such as SQL, SPARQL, or XQuery)
offer a convenient and explicit way for users to express their information
needs for a number of tasks. In this work, we present an approach to answer
these directly over text data without storing results in a database. We
specifically look at the case of knowledge bases where queries are over
entities and the relations between them. Our approach combines distributed
query answering (e.g. Triple Pattern Fragments) with models built for
extractive question answering. Importantly, by applying distributed querying
answering we are able to simplify the model learning problem. We train models
for a large portion (572) of the relations within Wikidata and achieve an
average 0.70 F1 measure across all models. We also present a systematic method
to construct the necessary training data for this task from knowledge graphs
and describe a prototype implementation.

37
The categories of this paper are: supr-con
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The categories of this paper are: chem-ph
21
The categories of this paper are: adap-org
14
The categories of this paper are: math-ph
26
The categories of this paper are: bayes-an
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The categories of this paper are: solv-int
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The categories of this paper are: q-bio
2
The categories of this paper are: math
6
The categories of this paper are: astro-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: atom-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: stat
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The categories of this paper are: q-fin
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The categories of this paper are: hep-lat
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The categories of this paper are: acc-phys