Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Generating Challenge Datasets for Task-Oriented Conversational Agents
  through Self-Play   End-to-end neural approaches are becoming increasingly common in
conversational scenarios due to their promising performances when provided with
sufficient amount of data. In this paper, we present a novel methodology to
address the interpretability of neural approaches in such scenarios by creating
challenge datasets using dialogue self-play over multiple tasks/intents.
Dialogue self-play allows generating large amount of synthetic data; by taking
advantage of the complete control over the generation process, we show how
neural approaches can be evaluated in terms of unseen dialogue patterns. We
propose several out-of-pattern test cases each of which introduces a natural
and unexpected user utterance phenomenon. As a proof of concept, we built a
single and a multiple memory network, and show that these two architectures
have diverse performances depending on the peculiar dialogue patterns.

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The categories of this paper are: physics
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The categories of this paper are: nucl-th
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The categories of this paper are: cond-mat
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The categories of this paper are: adap-org
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The categories of this paper are: chem-ph
13
The categories of this paper are: quant-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: q-bio
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The categories of this paper are: mtrl-th
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The categories of this paper are: stat
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The categories of this paper are: patt-sol
15
The categories of this paper are: nucl-ex
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The categories of this paper are: hep-ex
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The categories of this paper are: nlin
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The categories of this paper are: hep-th
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The categories of this paper are: q-fin