Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Generating Synthetic Data for Task-Oriented Semantic Parsing with
  Hierarchical Representations   Modern conversational AI systems support natural language understanding for a
wide variety of capabilities. While a majority of these tasks can be
accomplished using a simple and flat representation of intents and slots, more
sophisticated capabilities require complex hierarchical representations
supported by semantic parsing. State-of-the-art semantic parsers are trained
using supervised learning with data labeled according to a hierarchical schema
which might be costly to obtain or not readily available for a new domain. In
this work, we explore the possibility of generating synthetic data for neural
semantic parsing using a pretrained denoising sequence-to-sequence model (i.e.,
BART). Specifically, we first extract masked templates from the existing
labeled utterances, and then fine-tune BART to generate synthetic utterances
conditioning on the extracted templates. Finally, we use an auxiliary parser
(AP) to filter the generated utterances. The AP guarantees the quality of the
generated data. We show the potential of our approach when evaluating on the
Facebook TOP dataset for navigation domain.

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The categories of this paper are: supr-con
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The categories of this paper are: quant-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: nucl-ex
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The categories of this paper are: solv-int
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The categories of this paper are: comp-gas
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The categories of this paper are: q-fin
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The categories of this paper are: cond-mat
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The categories of this paper are: funct-an
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The categories of this paper are: econ
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The categories of this paper are: q-bio
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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: physics
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The categories of this paper are: chem-ph
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The categories of this paper are: cmp-lg