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README.md

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

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