Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A dataset for resolving referring expressions in spoken dialogue via
  contextual query rewrites (CQR)   We present Contextual Query Rewrite (CQR) a dataset for multi-domain
task-oriented spoken dialogue systems that is an extension of the Stanford
dialog corpus (Eric et al., 2017a). While previous approaches have addressed
the issue of diverse schemas by learning candidate transformations (Naik et
al., 2018), we instead model the reference resolution task as a user query
reformulation task, where the dialog state is serialized into a natural
language query that can be executed by the downstream spoken language
understanding system. In this paper, we describe our methodology for creating
the query reformulation extension to the dialog corpus, and present an initial
set of experiments to establish a baseline for the CQR task. We have released
the corpus to the public [1] to support further research in this area.

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The categories of this paper are: eess
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The categories of this paper are: adap-org
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The categories of this paper are: q-alg
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The categories of this paper are: plasm-ph
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The categories of this paper are: astro-ph
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The categories of this paper are: cmp-lg
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The categories of this paper are: hep-th
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The categories of this paper are: patt-sol
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The categories of this paper are: quant-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: nlin
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The categories of this paper are: comp-gas
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The categories of this paper are: hep-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: hep-ex