HumorAAC at SemEval-2020 Task 7: Assessing the Funniness of Edited News Headlines through Regression and Trump Mentions

Anna-Katharina Dick, Charlotte Weirich, Alla Kutkina


Abstract
In this paper we describe our contribution to the Semeval-2020 Humor Assessment task. We essentially use three different features that are passed into a ridge regression to determine a funniness score for an edited news headline: statistical, count-based features, semantic features and contextual information. For deciding which one of two given edited headlines is funnier, we additionally use scoring information and logistic regression. Our work was mostly concentrated on investigating features, rather than improving prediction based on pre-trained language models. The resulting system is task-specific, lightweight and performs above the majority baseline. Our experiments indicate that features related to socio-cultural context, in our case mentions of Donald Trump, generally perform better than context-independent features like headline length.
Anthology ID:
2020.semeval-1.133
Volume:
Proceedings of the Fourteenth Workshop on Semantic Evaluation
Month:
December
Year:
2020
Address:
Barcelona (online)
Venues:
COLING | SemEval
SIGs:
SIGLEX | SIGSEM
Publisher:
International Committee for Computational Linguistics
Note:
Pages:
1019–1025
Language:
URL:
https://aclanthology.org/2020.semeval-1.133
DOI:
10.18653/v1/2020.semeval-1.133
Bibkey:
Cite (ACL):
Anna-Katharina Dick, Charlotte Weirich, and Alla Kutkina. 2020. HumorAAC at SemEval-2020 Task 7: Assessing the Funniness of Edited News Headlines through Regression and Trump Mentions. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 1019–1025, Barcelona (online). International Committee for Computational Linguistics.
Cite (Informal):
HumorAAC at SemEval-2020 Task 7: Assessing the Funniness of Edited News Headlines through Regression and Trump Mentions (Dick et al., SemEval 2020)
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PDF:
https://preview.aclanthology.org/update-css-js/2020.semeval-1.133.pdf