Upload README.md

README.md

@@ -0,0 +1,332 @@
+---
+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:** jackh@allenai.org
+
+### Dataset Summary
+
+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.,
+```
+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.:
+
+```
+# 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},
+}
+```