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--- |
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annotations_creators: |
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- expert-generated |
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- crowdsourced |
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- found |
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language: |
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- en |
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language_creators: |
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- crowdsourced |
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- expert-generated |
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license: |
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- cc-by-4.0 |
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multilinguality: |
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- monolingual |
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pretty_name: newyorker_caption_contest |
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size_categories: |
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- 1K<n<10K |
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source_datasets: |
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- original |
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tags: |
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- humor |
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- caption contest |
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- new yorker |
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task_categories: |
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- image-to-text |
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- multiple-choice |
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- text-classification |
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- text-generation |
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- visual-question-answering |
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- other |
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- text2text-generation |
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task_ids: |
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- multi-class-classification |
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- language-modeling |
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- visual-question-answering |
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- explanation-generation |
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--- |
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|
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# Dataset Card for New Yorker Caption Contest Benchmarks |
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|
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## Table of Contents |
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- [Table of Contents](#table-of-contents) |
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- [Dataset Description](#dataset-description) |
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- [Dataset Summary](#dataset-summary) |
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- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) |
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- [Languages](#languages) |
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- [Dataset Structure](#dataset-structure) |
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- [Data Instances](#data-instances) |
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- [Data Fields](#data-fields) |
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- [Data Splits](#data-splits) |
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- [Dataset Creation](#dataset-creation) |
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- [Curation Rationale](#curation-rationale) |
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- [Source Data](#source-data) |
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- [Annotations](#annotations) |
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- [Personal and Sensitive Information](#personal-and-sensitive-information) |
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- [Considerations for Using the Data](#considerations-for-using-the-data) |
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- [Social Impact of Dataset](#social-impact-of-dataset) |
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- [Discussion of Biases](#discussion-of-biases) |
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- [Other Known Limitations](#other-known-limitations) |
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- [Additional Information](#additional-information) |
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- [Dataset Curators](#dataset-curators) |
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- [Licensing Information](#licensing-information) |
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- [Citation Information](#citation-information) |
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- [Contributions](#contributions) |
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|
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## Dataset Description |
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|
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- **Homepage:** [capcon.dev](https://www.capcon.dev) |
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- **Repository:** [https://github.com/jmhessel/caption_contest_corpus](https://github.com/jmhessel/caption_contest_corpus) |
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- **Paper:** [Do Androids Laugh at Electric Sheep? Humor "Understanding" Benchmarks from The New Yorker Caption Contest](https://arxiv.org/abs/2209.06293) |
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- **Leaderboard:** https://leaderboard.allenai.org/nycc-matching/ and https://leaderboard.allenai.org/nycc-ranking |
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- **Point of Contact:** jackh@allenai.org |
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|
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### Dataset Summary |
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|
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Data from: |
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[Do Androids Laugh at Electric Sheep? Humor "Understanding" Benchmarks from The New Yorker Caption Contest](https://arxiv.org/abs/2209.06293) |
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|
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``` |
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@article{hessel2022androids, |
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title={Do Androids Laugh at Electric Sheep? Humor "Understanding" Benchmarks from The New Yorker Caption Contest}, |
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author={Hessel, Jack and Marasovi{\'c}, Ana and Hwang, Jena D and Lee, Lillian and Da, Jeff and Zellers, Rowan and Mankoff, Robert and Choi, Yejin}, |
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journal={arXiv preprint arXiv:2209.06293}, |
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year={2022} |
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} |
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``` |
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If you use this dataset, we would appreciate you citing our work, but also -- several other papers that we build this corpus upon. See [Citation Information](#citation-information). |
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We challenge AI models to "demonstrate understanding" of the |
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sophisticated multimodal humor of The New Yorker Caption Contest. |
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Concretely, we develop three carefully circumscribed tasks for which |
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it suffices (but is not necessary) to grasp potentially complex and |
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unexpected relationships between image and caption, and similarly |
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complex and unexpected allusions to the wide varieties of human |
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experience. |
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### Supported Tasks and Leaderboards |
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Three tasks are supported: |
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- "Matching:" a model must recognize a caption written about a cartoon (vs. options that were not); |
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- "Quality ranking:" a model must evaluate the quality of a caption by scoring it more highly than a lower quality option from the same contest; |
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- "Explanation:" a model must explain why a given joke is funny. |
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There are no official leaderboards (yet). |
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### Languages |
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English |
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## Dataset Structure |
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Here's an example instance from Matching: |
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``` |
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{'caption_choices': ['Tell me about your childhood very quickly.', |
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"Believe me . . . it's what's UNDER the ground that's " |
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'most interesting.', |
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"Stop me if you've heard this one.", |
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'I have trouble saying no.', |
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'Yes, I see the train but I think we can beat it.'], |
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'contest_number': 49, |
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'entities': ['https://en.wikipedia.org/wiki/Rule_of_three_(writing)', |
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'https://en.wikipedia.org/wiki/Bar_joke', |
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'https://en.wikipedia.org/wiki/Religious_institute'], |
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'from_description': 'scene: a bar description: Two priests and a rabbi are ' |
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'walking into a bar, as the bartender and another patron ' |
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'look on. The bartender talks on the phone while looking ' |
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'skeptically at the incoming crew. uncanny: The scene ' |
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'depicts a very stereotypical "bar joke" that would be ' |
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'unlikely to be encountered in real life; the skepticism ' |
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'of the bartender suggests that he is aware he is seeing ' |
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'this trope, and is explaining it to someone on the ' |
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'phone. entities: Rule_of_three_(writing), Bar_joke, ' |
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'Religious_institute. choices A: Tell me about your ' |
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"childhood very quickly. B: Believe me . . . it's what's " |
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"UNDER the ground that's most interesting. C: Stop me if " |
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"you've heard this one. D: I have trouble saying no. E: " |
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'Yes, I see the train but I think we can beat it.', |
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'image': <PIL.JpegImagePlugin.JpegImageFile image mode=L size=323x231 at 0x7F34F283E9D0>, |
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'image_description': 'Two priests and a rabbi are walking into a bar, as the ' |
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'bartender and another patron look on. The bartender ' |
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'talks on the phone while looking skeptically at the ' |
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'incoming crew.', |
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'image_location': 'a bar', |
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'image_uncanny_description': 'The scene depicts a very stereotypical "bar ' |
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'joke" that would be unlikely to be encountered ' |
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'in real life; the skepticism of the bartender ' |
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'suggests that he is aware he is seeing this ' |
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'trope, and is explaining it to someone on the ' |
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'phone.', |
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'instance_id': '21125bb8787b4e7e82aa3b0a1cba1571', |
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'label': 'C', |
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'n_tokens_label': 1, |
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'questions': ['What is the bartender saying on the phone in response to the ' |
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'living, breathing, stereotypical bar joke that is unfolding?']} |
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``` |
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The label "C" indicates that the 3rd choice in the `caption_choices` is correct. |
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Here's an example instance from Ranking (in the from pixels setting --- though, this is also available in the from description setting) |
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``` |
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{'caption_choices': ['I guess I misunderstood when you said long bike ride.', |
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'Does your divorce lawyer have any other cool ideas?'], |
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'contest_number': 582, |
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'image': <PIL.JpegImagePlugin.JpegImageFile image mode=L size=600x414 at 0x7F8FF9F96610>, |
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'instance_id': 'dd1c214a1ca3404aa4e582c9ce50795a', |
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'label': 'A', |
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'n_tokens_label': 1, |
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'winner_source': 'official_winner'} |
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``` |
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the label indicates that the first caption choice ("A", here) in the `caption_choices` list was more highly rated. |
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Here's an example instance from Explanation: |
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``` |
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{'caption_choices': 'The classics can be so intimidating.', |
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'contest_number': 752, |
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'entities': ['https://en.wikipedia.org/wiki/Literature', |
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'https://en.wikipedia.org/wiki/Solicitor'], |
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'from_description': 'scene: a road description: Two people are walking down a ' |
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'path. A number of giant books have surrounded them. ' |
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'uncanny: There are book people in this world. entities: ' |
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'Literature, Solicitor. caption: The classics can be so ' |
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'intimidating.', |
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'image': <PIL.JpegImagePlugin.JpegImageFile image mode=L size=800x706 at 0x7F90003D0BB0>, |
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'image_description': 'Two people are walking down a path. A number of giant ' |
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'books have surrounded them.', |
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'image_location': 'a road', |
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'image_uncanny_description': 'There are book people in this world.', |
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'instance_id': 'eef9baf450e2fab19b96facc128adf80', |
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'label': 'A play on the word intimidating --- usually if the classics (i.e., ' |
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'classic novels) were to be intimidating, this would mean that they ' |
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'are intimidating to read due to their length, complexity, etc. But ' |
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'here, they are surrounded by anthropomorphic books which look ' |
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'physically intimidating, i.e., they are intimidating because they ' |
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'may try to beat up these people.', |
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'n_tokens_label': 59, |
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'questions': ['What do the books want?']} |
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``` |
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The label is an explanation of the joke, which serves as the autoregressive target. |
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|
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### Data Instances |
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See above |
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### Data Fields |
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See above |
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### Data Splits |
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Data splits can be accessed as: |
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``` |
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from datasets import load_dataset |
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dset = load_dataset("jmhessel/newyorker_caption_contest", "matching") |
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dset = load_dataset("jmhessel/newyorker_caption_contest", "ranking") |
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dset = load_dataset("jmhessel/newyorker_caption_contest", "explanation") |
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``` |
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Or, in the from pixels setting, e.g., |
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``` |
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from datasets import load_dataset |
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dset = load_dataset("jmhessel/newyorker_caption_contest", "ranking_from_pixels") |
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``` |
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Because the dataset is small, we reported in 5-fold cross-validation setting initially. The default splits are split 0. You can access the other splits, e.g.: |
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``` |
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from datasets import load_dataset |
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# the 4th data split |
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dset = load_dataset("jmhessel/newyorker_caption_contest", "explanation_4") |
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``` |
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|
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## Dataset Creation |
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Full details are in the paper. |
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### Curation Rationale |
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See the paper for rationale/motivation. |
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### Source Data |
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See citation below. We combined 3 sources of data, and added significant annotations of our own. |
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#### Initial Data Collection and Normalization |
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Full details are in the paper. |
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#### Who are the source language producers? |
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We paid crowdworkers $15/hr to annotate the corpus. |
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In addition, significant annotation efforts were conducted by the authors of this work. |
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### Annotations |
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Full details are in the paper. |
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#### Annotation process |
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Full details are in the paper. |
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#### Who are the annotators? |
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A mix of crowdworks and authors of this paper. |
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### Personal and Sensitive Information |
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Has been redacted from the dataset. Images are published in the New Yorker already. |
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## Considerations for Using the Data |
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### Social Impact of Dataset |
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It's plausible that humor could perpetuate negative stereotypes. The jokes in this corpus are a mix of crowdsourced entries that are highly rated, and ones published in the new yorker. |
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### Discussion of Biases |
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Humor is subjective, and some of the jokes may be considered offensive. The images may contain adult themes and minor cartoon nudity. |
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### Other Known Limitations |
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More details are in the paper |
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## Additional Information |
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### Dataset Curators |
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The dataset was curated by researchers at AI2 |
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### Licensing Information |
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The annotations we provide are CC-BY-4.0. See www.capcon.dev for more info. |
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### Citation Information |
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|
|
|
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``` |
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@article{hessel2022androids, |
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title={Do Androids Laugh at Electric Sheep? Humor "Understanding" Benchmarks from The New Yorker Caption Contest}, |
|
author={Hessel, Jack and Marasovi{\'c}, Ana and Hwang, Jena D and Lee, Lillian and Da, Jeff and Zellers, Rowan and Mankoff, Robert and Choi, Yejin}, |
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journal={arXiv preprint arXiv:2209.06293}, |
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year={2022} |
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} |
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``` |
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|
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Our data contributions are: |
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|
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- The cartoon-level annotations; |
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- The joke explanations; |
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- and the framing of the tasks |
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We release these data we contribute under CC-BY (see DATASET_LICENSE). If you find this data useful in your work, in addition to citing our contributions, please also cite the following, from which the cartoons/captions in our corpus are derived: |
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|
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``` |
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@misc{newyorkernextmldataset, |
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author={Jain, Lalit and Jamieson, Kevin and Mankoff, Robert and Nowak, Robert and Sievert, Scott}, |
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title={The {N}ew {Y}orker Cartoon Caption Contest Dataset}, |
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year={2020}, |
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url={https://nextml.github.io/caption-contest-data/} |
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} |
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|
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@inproceedings{radev-etal-2016-humor, |
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title = "Humor in Collective Discourse: Unsupervised Funniness Detection in The {New Yorker} Cartoon Caption Contest", |
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author = "Radev, Dragomir and |
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Stent, Amanda and |
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Tetreault, Joel and |
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Pappu, Aasish and |
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Iliakopoulou, Aikaterini and |
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Chanfreau, Agustin and |
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de Juan, Paloma and |
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Vallmitjana, Jordi and |
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Jaimes, Alejandro and |
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Jha, Rahul and |
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Mankoff, Robert", |
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booktitle = "LREC", |
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year = "2016", |
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} |
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|
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@inproceedings{shahaf2015inside, |
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title={Inside jokes: Identifying humorous cartoon captions}, |
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author={Shahaf, Dafna and Horvitz, Eric and Mankoff, Robert}, |
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booktitle={KDD}, |
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year={2015}, |
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} |
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``` |