@inproceedings{hossain-etal-2020-semeval,
    title = "{S}em{E}val-2020 Task 7: Assessing Humor in Edited News Headlines",
    author = "Hossain, Nabil  and
      Krumm, John  and
      Gamon, Michael  and
      Kautz, Henry",
    editor = "Herbelot, Aurelie  and
      Zhu, Xiaodan  and
      Palmer, Alexis  and
      Schneider, Nathan  and
      May, Jonathan  and
      Shutova, Ekaterina",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2020.semeval-1.98/",
    doi = "10.18653/v1/2020.semeval-1.98",
    pages = "746--758",
    abstract = "This paper describes the SemEval-2020 shared task {\textquotedblleft}Assessing Humor in Edited News Headlines.{\textquotedblright} The task`s dataset contains news headlines in which short edits were applied to make them funny, and the funniness of these edited headlines was rated using crowdsourcing. This task includes two subtasks, the first of which is to estimate the funniness of headlines on a humor scale in the interval 0-3. The second subtask is to predict, for a pair of edited versions of the same original headline, which is the funnier version. To date, this task is the most popular shared computational humor task, attracting 48 teams for the first subtask and 31 teams for the second."
}