SemEval-2020 Task 7: Assessing Humor in Edited News Headlines

• URL: http://arxiv.org/abs/2008.00304v1
• Date: Sat, 1 Aug 2020 17:34:37 GMT
• Title: SemEval-2020 Task 7: Assessing Humor in Edited News Headlines
• Authors: Nabil Hossain, John Krumm, Michael Gamon and Henry Kautz
• Abstract summary: This paper describes the SemEval-2020 shared task "Assessing Humor in Edited News Headlines" 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. 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.
• Score: 9.78014714425501
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper describes the SemEval-2020 shared task "Assessing Humor in Edited News Headlines." 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.

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