Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: What makes a good conversation? How controllable attributes affect human
  judgments   A good conversation requires balance -- between simplicity and detail;
staying on topic and changing it; asking questions and answering them. Although
dialogue agents are commonly evaluated via human judgments of overall quality,
the relationship between quality and these individual factors is less
well-studied. In this work, we examine two controllable neural text generation
methods, conditional training and weighted decoding, in order to control four
important attributes for chitchat dialogue: repetition, specificity,
response-relatedness and question-asking. We conduct a large-scale human
evaluation to measure the effect of these control parameters on multi-turn
interactive conversations on the PersonaChat task. We provide a detailed
analysis of their relationship to high-level aspects of conversation, and show
that by controlling combinations of these variables our models obtain clear
improvements in human quality judgments.

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The categories of this paper are: patt-sol
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The categories of this paper are: q-fin
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The categories of this paper are: econ
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The categories of this paper are: hep-ph
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The categories of this paper are: q-alg
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The categories of this paper are: dg-ga
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The categories of this paper are: quant-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: math-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: ao-sci
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The categories of this paper are: mtrl-th
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The categories of this paper are: gr-qc
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The categories of this paper are: astro-ph
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The categories of this paper are: stat
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The categories of this paper are: math