Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Uncertainty and Surprisal Jointly Deliver the Punchline: Exploiting
  Incongruity-Based Features for Humor Recognition   Humor recognition has been widely studied as a text classification problem
using data-driven approaches. However, most existing work does not examine the
actual joke mechanism to understand humor. We break down any joke into two
distinct components: the set-up and the punchline, and further explore the
special relationship between them. Inspired by the incongruity theory of humor,
we model the set-up as the part developing semantic uncertainty, and the
punchline disrupting audience expectations. With increasingly powerful language
models, we were able to feed the set-up along with the punchline into the GPT-2
language model, and calculate the uncertainty and surprisal values of the
jokes. By conducting experiments on the SemEval 2021 Task 7 dataset, we found
that these two features have better capabilities of telling jokes from
non-jokes, compared with existing baselines.

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The categories of this paper are: math-ph
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The categories of this paper are: econ
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The categories of this paper are: stat
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The categories of this paper are: alg-geom
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The categories of this paper are: dg-ga
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The categories of this paper are: hep-lat
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The categories of this paper are: gr-qc
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The categories of this paper are: chem-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: funct-an
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The categories of this paper are: q-bio
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The categories of this paper are: math
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The categories of this paper are: supr-con
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The categories of this paper are: physics
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The categories of this paper are: bayes-an
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The categories of this paper are: acc-phys