
# 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.
task_categories:question-answering task_ids:multiple-choice-qa annotations_creators:crowdsourced annotations_creators:machine-generated language_creators:crowdsourced language_creators:found multilinguality:monolingual size_categories:10K<n<100K source_datasets:original language:en license:unknown arxiv:1909.03065 region:us
{"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}}
TAGS
#task_categories-question-answering #task_ids-multiple-choice-qa #annotations_creators-crowdsourced #annotations_creators-machine-generated #language_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-unknown #arxiv-1909.03065 #region-us 


# Dataset Card for MC-TACO

## Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks and Leaderboards
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations
  - Personal and Sensitive Information
- Considerations for Using the Data
  - Social Impact of Dataset
  - Discussion of Biases
  - Other Known Limitations
- Additional Information
  - Dataset Curators
  - Licensing Information
  - Citation Information
  - Contributions

## Dataset Description

- Homepage: MC-TACO
- Repository: Github repository
- Paper: "Going on a vacation" takes longer than "Going for a walk": A Study of Temporal Commonsense Understanding
- Leaderboard: AI2 Leaderboard

### 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:



### 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 (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



#### Who are the source language producers?



### 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: question generation, question verification, candidate answer expansion and answer labeling.

#### Who are the annotators?

Paid crowdsourcers.

### Personal and Sensitive Information



## Considerations for Using the Data

### Social Impact of Dataset



### Discussion of Biases



### Other Known Limitations



## Additional Information

### Dataset Curators



### Licensing Information

Unknwon





### Contributions

Thanks to @VictorSanh for adding this dataset.
# Dataset Card for MC-TACO ## Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks and Leaderboards
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations
  - Personal and Sensitive Information
- Considerations for Using the Data
  - Social Impact of Dataset
  - Discussion of Biases
  - Other Known Limitations
- Additional Information
  - Dataset Curators
  - Licensing Information
  - Citation Information
  - Contributions ## Dataset Description

- Homepage: MC-TACO
- Repository: Github repository
- Paper: "Going on a vacation" takes longer than "Going for a walk": A Study of Temporal Commonsense Understanding
- Leaderboard: AI2 Leaderboard ### 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: ### 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 (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 #### Who are the source language producers? ### 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: question generation, question verification, candidate answer expansion and answer labeling. #### Who are the annotators?

Paid crowdsourcers. ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information

Unknwon ### Contributions

Thanks to @VictorSanh for adding this dataset.
TAGS
#task_categories-question-answering #task_ids-multiple-choice-qa #annotations_creators-crowdsourced #annotations_creators-machine-generated #language_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-unknown #arxiv-1909.03065 #region-us 
 # Dataset Card for MC-TACO ## Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks and Leaderboards
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations
  - Personal and Sensitive Information
- Considerations for Using the Data
  - Social Impact of Dataset
  - Discussion of Biases
  - Other Known Limitations
- Additional Information
  - Dataset Curators
  - Licensing Information
  - Citation Information
  - Contributions ## Dataset Description

- Homepage: MC-TACO
- Repository: Github repository
- Paper: "Going on a vacation" takes longer than "Going for a walk": A Study of Temporal Commonsense Understanding
- Leaderboard: AI2 Leaderboard ### 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: ### 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 (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 #### Who are the source language producers? ### 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: question generation, question verification, candidate answer expansion and answer labeling. #### Who are the annotators?

Paid crowdsourcers. ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators ### Licensing Information

Unknwon ### Contributions

Thanks to @VictorSanh for adding this dataset.
passage: TAGS
#task_categories-question-answering #task_ids-multiple-choice-qa #annotations_creators-crowdsourced #annotations_creators-machine-generated #language_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-unknown #arxiv-1909.03065 #region-us 
# Dataset Card for MC-TACO## Table of Contents
- Dataset Description
  - Dataset Summary
  - Supported Tasks and Leaderboards
  - Languages
- Dataset Structure
  - Data Instances
  - Data Fields
  - Data Splits
- Dataset Creation
  - Curation Rationale
  - Source Data
  - Annotations
  - Personal and Sensitive Information
- Considerations for Using the Data
  - Social Impact of Dataset
  - Discussion of Biases
  - Other Known Limitations
- Additional Information
  - Dataset Curators
  - Licensing Information
  - Citation Information
  - Contributions## Dataset Description

- Homepage: MC-TACO
- Repository: Github repository
- Paper: "Going on a vacation" takes longer than "Going for a walk": A Study of Temporal Commonsense Understanding
- Leaderboard: AI2 Leaderboard### 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. passage: ### 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:### 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 (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#### Who are the source language producers?### Annotations

From the original paper:

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