Datasets:
mc_taco

	---
	annotations_creators:
	- crowdsourced
	- machine-generated
	language_creators:
	- crowdsourced
	- found
	language:
	- en
	license:
	- unknown
	multilinguality:
	- monolingual
	size_categories:
	- 10K<n<100K
	source_datasets:
	- original
	task_categories:
	- question-answering
	task_ids:
	- multiple-choice-qa
	paperswithcode_id: mc-taco
	pretty_name: MC-TACO
	dataset_info:
	features:
	- name: sentence
	dtype: string
	- name: question
	dtype: string
	- name: answer
	dtype: string
	- name: label
	dtype:
	class_label:
	names:
	'0': 'no'
	'1': 'yes'
	- name: category
	dtype:
	class_label:
	names:
	'0': Event Duration
	'1': Event Ordering
	'2': Frequency
	'3': Typical Time
	'4': Stationarity
	config_name: plain_text
	splits:
	- name: test
	num_bytes: 1785553
	num_examples: 9442
	- name: validation
	num_bytes: 713023
	num_examples: 3783
	download_size: 2385137
	dataset_size: 2498576
	---

	# Dataset Card for MC-TACO

	## 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: [MC-TACO](https://cogcomp.seas.upenn.edu/page/resource_view/125)
	- Repository: [Github repository](https://github.com/CogComp/MCTACO)
	- Paper: ["Going on a vacation" takes longer than "Going for a walk": A Study of Temporal Commonsense Understanding](https://arxiv.org/abs/1909.03065)
	- Leaderboard: [AI2 Leaderboard](https://leaderboard.allenai.org/mctaco)

	### Dataset Summary

	MC-TACO (Multiple Choice TemporAl COmmonsense) is a dataset of 13k question-answer pairs that require temporal commonsense comprehension. A system receives a sentence providing context information, a question designed to require temporal commonsense knowledge, and multiple candidate answers. More than one candidate answer can be plausible.

	### Supported Tasks and Leaderboards

	The task is framed as binary classification: givent he context, the question, and the candidate answer, the task is to determine whether the candidate answer is plausible ("yes") or not ("no").

	Performance is measured using two metrics:

	- Exact Match -- the average number of questions for which all the candidate answers are predicted correctly.
	- F1 -- is slightly more relaxed than EM. It measures the overlap between one’s predictions and the ground truth, by computing the geometric mean of Precision and Recall.

	### Languages

	The text in the dataset is in English. The associated BCP-47 code is `en`.

	## Dataset Structure

	### Data Instances

	An example looks like this:

	```
	{
	"sentence": "However, more recently, it has been suggested that it may date from earlier than Abdalonymus' death.",
	"question": "How often did Abdalonymus die?",
	"answer": "every two years",
	"label": "no",
	"category": "Frequency",
	}
	```

	### Data Fields

	All fields are strings:
	- `sentence`: a sentence (or context) on which the question is based
	- `question`: a question querying some temporal commonsense knowledge
	- `answer`: a potential answer to the question (all lowercased)
	- `label`: whether the answer is a correct. "yes" indicates the answer is correct/plaussible, "no" otherwise
	- `category`: the temporal category the question belongs to (among "Event Ordering", "Event Duration", "Frequency", "Stationarity", and "Typical Time")

	### Data Splits

	The development set contains 561 questions and 3,783 candidate answers. The test set contains 1,332 questions and 9,442 candidate answers.

	From the original repository:

	Note that there is no training data, and we provide the dev set as the only source of supervision. The rationale is that we believe a successful system has to bring in a huge amount of world knowledge and derive commonsense understandings prior to the current task evaluation. We therefore believe that it is not reasonable to expect a system to be trained solely on this data, and we think of the development data as only providing a definition of the task.

	## Dataset Creation

	### Curation Rationale

	MC-TACO is used as a testbed to study the temporal commonsense understanding on NLP systems.

	### Source Data

	From the original paper:

	The context sentences are randomly selected from [MultiRC](https://www.aclweb.org/anthology/N18-1023/) (from each of its 9 domains). For each sentence, we use crowdsourcing on Amazon Mechanical Turk to collect questions and candidate answers (both correct and wrong ones).

	#### Initial Data Collection and Normalization

	[More Information Needed]

	#### Who are the source language producers?

	[More Information Needed]

	### Annotations

	From the original paper:

	To ensure the quality of the results, we limit the annotations to native speakers and use qualification tryouts.

	#### Annotation process

	The crowdsourced construction/annotation of the dataset follows 4 steps described in Section 3 of the [paper](https://arxiv.org/abs/1909.03065): question generation, question verification, candidate answer expansion and answer labeling.

	#### Who are the annotators?

	Paid crowdsourcers.

	### Personal and Sensitive Information

	[More Information Needed]

	## Considerations for Using the Data

	### Social Impact of Dataset

	[More Information Needed]

	### Discussion of Biases

	[More Information Needed]

	### Other Known Limitations

	[More Information Needed]

	## Additional Information

	### Dataset Curators

	[More Information Needed]

	### Licensing Information

	Unknwon

	### Citation Information

	```
	@inproceedings{ZKNR19,
	author = {Ben Zhou, Daniel Khashabi, Qiang Ning and Dan Roth},
	title = {“Going on a vacation” takes longer than “Going for a walk”: A Study of Temporal Commonsense Understanding },
	booktitle = {EMNLP},
	year = {2019},
	}
	```

	### Contributions

	Thanks to [@VictorSanh](https://github.com/VictorSanh) for adding this dataset.