Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multi-Domain Spoken Language Understanding Using Domain- and Task-Aware
  Parameterization   Spoken language understanding has been addressed as a supervised learning
problem, where a set of training data is available for each domain. However,
annotating data for each domain is both financially costly and non-scalable so
we should fully utilize information across all domains. One existing approach
solves the problem by conducting multi-domain learning, using shared parameters
for joint training across domains. We propose to improve the parameterization
of this method by using domain-specific and task-specific model parameters to
improve knowledge learning and transfer. Experiments on 5 domains show that our
model is more effective for multi-domain SLU and obtain the best results. In
addition, we show its transferability by outperforming the prior best model by
12.4\% when adapting to a new domain with little data.

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The categories of this paper are: adap-org
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The categories of this paper are: alg-geom
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The categories of this paper are: math-ph
34
The categories of this paper are: chem-ph
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The categories of this paper are: hep-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: econ
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The categories of this paper are: atom-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: nlin
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The categories of this paper are: physics
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The categories of this paper are: plasm-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: acc-phys
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The categories of this paper are: eess
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The categories of this paper are: hep-lat