@inproceedings{hessel-etal-2023-androids,
    title = "Do Androids Laugh at Electric Sheep? Humor {``}Understanding{''} Benchmarks from The New Yorker Caption Contest",
    author = "Hessel, Jack  and
      Marasovic, Ana  and
      Hwang, Jena D.  and
      Lee, Lillian  and
      Da, Jeff  and
      Zellers, Rowan  and
      Mankoff, Robert  and
      Choi, Yejin",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.41",
    doi = "10.18653/v1/2023.acl-long.41",
    pages = "688--714",
    abstract = "Large neural networks can now generate jokes, but do they really {``}understand{''} humor? We challenge AI models with three tasks derived from the New Yorker Cartoon Caption Contest: matching a joke to a cartoon, identifying a winning caption, and explaining why a winning caption is funny. These tasks encapsulate progressively more sophisticated aspects of {``}understanding{''} a cartoon; key elements are the complex, often surprising relationships between images and captions and the frequent inclusion of indirect and playful allusions to human experience and culture. We investigate both multimodal and language-only models: the former are challenged with the cartoon images directly, while the latter are given multifaceted descriptions of the visual scene to simulate human-level visual understanding. We find that both types of models struggle at all three tasks. For example, our best multimodal models fall 30 accuracy points behind human performance on the matching task, and, even when provided ground-truth visual scene descriptors, human-authored explanations are preferred head-to-head over the best machine-authored ones (few-shot GPT-4) in more than 2/3 of cases. We release models, code, leaderboard, and corpus, which includes newly-gathered annotations describing the image{'}s locations/entities, what{'}s unusual in the scene, and an explanation of the joke.",
}

Large neural networks can now generate jokes, but do they really {``}understand{''} humor? We challenge AI models with three tasks derived from the New Yorker Cartoon Caption Contest: matching a joke to a cartoon, identifying a winning caption, and explaining why a winning caption is funny. These tasks encapsulate progressively more sophisticated aspects of {``}understanding{''} a cartoon; key elements are the complex, often surprising relationships between images and captions and the frequent inclusion of indirect and playful allusions to human experience and culture. We investigate both multimodal and language-only models: the former are challenged with the cartoon images directly, while the latter are given multifaceted descriptions of the visual scene to simulate human-level visual understanding. We find that both types of models struggle at all three tasks. For example, our best multimodal models fall 30 accuracy points behind human performance on the matching task, and, even when provided ground-truth visual scene descriptors, human-authored explanations are preferred head-to-head over the best machine-authored ones (few-shot GPT-4) in more than 2/3 of cases. We release models, code, leaderboard, and corpus, which includes newly-gathered annotations describing the image{'}s locations/entities, what{'}s unusual in the scene, and an explanation of the joke.
Hessel, Jack Marasovic, Ana Hwang, Jena D. Lee, Lillian Da, Jeff Zellers, Rowan Mankoff, Robert Choi, Yejin
Do Androids Laugh at Electric Sheep? Humor “Understanding” Benchmarks from The New Yorker Caption Contest