Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Augmenting Slot Values and Contexts for Spoken Language Understanding
  with Pretrained Models   Spoken Language Understanding (SLU) is one essential step in building a
dialogue system. Due to the expensive cost of obtaining the labeled data, SLU
suffers from the data scarcity problem. Therefore, in this paper, we focus on
data augmentation for slot filling task in SLU. To achieve that, we aim at
generating more diverse data based on existing data. Specifically, we try to
exploit the latent language knowledge from pretrained language models by
finetuning them. We propose two strategies for finetuning process: value-based
and context-based augmentation. Experimental results on two public SLU datasets
have shown that compared with existing data augmentation methods, our proposed
method can generate more diverse sentences and significantly improve the
performance on SLU.

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The categories of this paper are: atom-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: nlin
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The categories of this paper are: math
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The categories of this paper are: econ
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The categories of this paper are: patt-sol
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The categories of this paper are: solv-int
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The categories of this paper are: adap-org
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The categories of this paper are: chem-ph
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The categories of this paper are: physics
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The categories of this paper are: cond-mat
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The categories of this paper are: alg-geom
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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: ao-sci
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The categories of this paper are: q-alg