Papers with baseline classifiers

2 papers
“President Vows to Cut <Taxes> Hair”: Dataset and Analysis of Creative Text Editing for Humorous Headlines (N19-1)

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Challenge: Existing datasets address specific humor templates, such as funny one-liners and filling in Mad Libs R.
Approach: They introduce a dataset for research in computational humor that uses crowdsourced editing techniques to create funny headlines.
Outcome: The new dataset supports classic theories of humor, including incongruity, superiority, setup/punchline.
WikiTalkEdit: A Dataset for modeling Editors’ behaviors on Wikipedia (2021.naacl-main)

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Challenge: Using the WikiTalkEdit dataset, we show how positive emotion and the use of first-person pronouns predict a positive emotional change in a Wikipedia contributor.
Approach: They introduce and analyze WikiTalkEdit, a dataset of conversations and edit histories from Wikipedia, for research in online cooperation and conversation modeling.
Outcome: The proposed dataset supports the classic understanding of style matching, where positive emotion and the use of first-person pronouns predict a positive emotional change in a Wikipedia contributor.

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