--- annotations_creators: - expert-generated - crowdsourced - found language_creators: - crowdsourced - expert-generated language: - en license: - cc-by-4.0 multilinguality: - monolingual size_categories: - 1K, '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': , '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': , '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("jmhessel/newyorker_caption_contest", "matching") dset = load_dataset("jmhessel/newyorker_caption_contest", "ranking") dset = load_dataset("jmhessel/newyorker_caption_contest", "explanation") ``` Or, in the from pixels setting, e.g., ``` from datasets import load_dataset dset = load_dataset("jmhessel/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("jmhessel/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}, } ```