Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Efficient Deployment of Conversational Natural Language Interfaces over
  Databases   Many users communicate with chatbots and AI assistants in order to help them
with various tasks. A key component of the assistant is the ability to
understand and answer a user's natural language questions for
question-answering (QA). Because data can be usually stored in a structured
manner, an essential step involves turning a natural language question into its
corresponding query language. However, in order to train most natural
language-to-query-language state-of-the-art models, a large amount of training
data is needed first. In most domains, this data is not available and
collecting such datasets for various domains can be tedious and time-consuming.
In this work, we propose a novel method for accelerating the training dataset
collection for developing the natural language-to-query-language machine
learning models. Our system allows one to generate conversational multi-term
data, where multiple turns define a dialogue session, enabling one to better
utilize chatbot interfaces. We train two current state-of-the-art NL-to-QL
models, on both an SQL and SPARQL-based datasets in order to showcase the
adaptability and efficacy of our created data.

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The categories of this paper are: q-bio
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The categories of this paper are: plasm-ph
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The categories of this paper are: math-ph
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The categories of this paper are: funct-an
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The categories of this paper are: cmp-lg
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The categories of this paper are: stat
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The categories of this paper are: bayes-an
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The categories of this paper are: chem-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: hep-th
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The categories of this paper are: nlin
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The categories of this paper are: alg-geom
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The categories of this paper are: nucl-th
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The categories of this paper are: dg-ga
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The categories of this paper are: astro-ph
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The categories of this paper are: chao-dyn