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        <title>Cards Against AI: Predicting Humor in a Fill-in-the-blank Party Game</title>
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            <namePart type="given">Yoav</namePart>
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    <abstract>Humor is an inherently social phenomenon, with humorous utterances shaped by what is socially and culturally accepted. Understanding humor is an important NLP challenge, with many applications to human-computer interactions. In this work we explore humor in the context of Cards Against Humanity – a party game where players complete fill-in-the-blank statements using cards that can be offensive or politically incorrect. We introduce a novel dataset of 300,000 online games of Cards Against Humanity, including 785K unique jokes, analyze it and provide insights. We trained machine learning models to predict the winning joke per game, achieving performance twice as good (20%) as random, even without any user information. On the more difficult task of judging novel cards, we see the models’ ability to generalize is moderate. Interestingly, we find that our models are primarily focused on punchline card, with the context having little impact. Analyzing feature importance, we observe that short, crude, juvenile punchlines tend to win.</abstract>
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%0 Conference Proceedings
%T Cards Against AI: Predicting Humor in a Fill-in-the-blank Party Game
%A Ofer, Dan
%A Shahaf, Dafna
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F ofer-shahaf-2022-cards
%X Humor is an inherently social phenomenon, with humorous utterances shaped by what is socially and culturally accepted. Understanding humor is an important NLP challenge, with many applications to human-computer interactions. In this work we explore humor in the context of Cards Against Humanity – a party game where players complete fill-in-the-blank statements using cards that can be offensive or politically incorrect. We introduce a novel dataset of 300,000 online games of Cards Against Humanity, including 785K unique jokes, analyze it and provide insights. We trained machine learning models to predict the winning joke per game, achieving performance twice as good (20%) as random, even without any user information. On the more difficult task of judging novel cards, we see the models’ ability to generalize is moderate. Interestingly, we find that our models are primarily focused on punchline card, with the context having little impact. Analyzing feature importance, we observe that short, crude, juvenile punchlines tend to win.
%R 10.18653/v1/2022.findings-emnlp.394
%U https://aclanthology.org/2022.findings-emnlp.394
%U https://doi.org/10.18653/v1/2022.findings-emnlp.394
%P 5397-5403

Markdown (Informal)

[Cards Against AI: Predicting Humor in a Fill-in-the-blank Party Game](https://aclanthology.org/2022.findings-emnlp.394) (Ofer & Shahaf, Findings 2022)

• Cards Against AI: Predicting Humor in a Fill-in-the-blank Party Game (Ofer & Shahaf, Findings 2022)

ACL

• Dan Ofer and Dafna Shahaf. 2022. Cards Against AI: Predicting Humor in a Fill-in-the-blank Party Game. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 5397–5403, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.