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

Nabil Hossain, John Krumm, Michael Gamon, Henry Kautz

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.
Anthology ID:
2020.semeval-1.98
Volume:
Proceedings of the Fourteenth Workshop on Semantic Evaluation
Month:
December
Year:
2020
Address:
Barcelona (online)
Editors:
Aurelie Herbelot, Xiaodan Zhu, Alexis Palmer, Nathan Schneider, Jonathan May, Ekaterina Shutova
Venue:
SemEval
SIG:
SIGLEX
Publisher:
International Committee for Computational Linguistics
Note:
Pages:
746–758
Language:
URL:
https://aclanthology.org/2020.semeval-1.98
DOI:
10.18653/v1/2020.semeval-1.98
Award:
 Best Task
Bibkey:
Cite (ACL):
Nabil Hossain, John Krumm, Michael Gamon, and Henry Kautz. 2020. SemEval-2020 Task 7: Assessing Humor in Edited News Headlines. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 746–758, Barcelona (online). International Committee for Computational Linguistics.
Cite (Informal):
SemEval-2020 Task 7: Assessing Humor in Edited News Headlines (Hossain et al., SemEval 2020)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-4/2020.semeval-1.98.pdf
Data
Humicroedit
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