Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multi-domain Dialog State Tracking using Recurrent Neural Networks   Dialog state tracking is a key component of many modern dialog systems, most
of which are designed with a single, well-defined domain in mind. This paper
shows that dialog data drawn from different dialog domains can be used to train
a general belief tracking model which can operate across all of these domains,
exhibiting superior performance to each of the domain-specific models. We
propose a training procedure which uses out-of-domain data to initialise belief
tracking models for entirely new domains. This procedure leads to improvements
in belief tracking performance regardless of the amount of in-domain data
available for training the model.

10
The categories of this paper are: q-bio
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The categories of this paper are: q-fin
29
The categories of this paper are: funct-an
8
The categories of this paper are: hep-ex
37
The categories of this paper are: supr-con
27
The categories of this paper are: comp-gas
23
The categories of this paper are: solv-int
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The categories of this paper are: gr-qc
26
The categories of this paper are: bayes-an
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The categories of this paper are: stat
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The categories of this paper are: patt-sol
4
The categories of this paper are: cond-mat
36
The categories of this paper are: cmp-lg
20
The categories of this paper are: acc-phys
14
The categories of this paper are: math-ph
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The categories of this paper are: hep-lat