# Datasets:mc_taco

Languages: English
Multilinguality: monolingual
Size Categories: 10K<n<100K
Language Creators: crowdsourced found
Source Datasets: original
ArXiv:
Dataset Preview
sentence (string)question (string)answer (string)label (class label)category (class label)
"Durer's father died in 1502, and his mother died in 1513."
"How long was his mother ill?"
"she was ill for 30 seconds"
0 (no)
0 (Event Duration)
"Durer's father died in 1502, and his mother died in 1513."
"How long was his mother ill?"
"six centuries"
0 (no)
0 (Event Duration)
"Durer's father died in 1502, and his mother died in 1513."
"How long was his mother ill?"
"she was ill for 90 years"
0 (no)
0 (Event Duration)
"Durer's father died in 1502, and his mother died in 1513."
"How long was his mother ill?"
"6 months"
1 (yes)
0 (Event Duration)
"Durer's father died in 1502, and his mother died in 1513."
"How long was his mother ill?"
"six minutes"
0 (no)
0 (Event Duration)
"Durer's father died in 1502, and his mother died in 1513."
"How long was his mother ill?"
"she was ill for 30 years"
0 (no)
0 (Event Duration)
"Durer's father died in 1502, and his mother died in 1513."
"How long was his mother ill?"
"six months"
1 (yes)
0 (Event Duration)
"Durer's father died in 1502, and his mother died in 1513."
"How long was his mother ill?"
"she was ill for 2 years"
1 (yes)
0 (Event Duration)
"Durer's father died in 1502, and his mother died in 1513."
"How long was his mother ill?"
"3 minutes"
0 (no)
0 (Event Duration)
"Durer's father died in 1502, and his mother died in 1513."
"How long was his mother ill?"
"6 centuries"
0 (no)
0 (Event Duration)
"Durer's father died in 1502, and his mother died in 1513."
"How long was his mother ill?"
"1 minutes"
0 (no)
0 (Event Duration)
"Durer's father died in 1502, and his mother died in 1513."
"How long was his mother ill?"
"3 centuries"
0 (no)
0 (Event Duration)
"Durer's father died in 1502, and his mother died in 1513."
"How long was his mother ill?"
"she was ill for 30 hours"
0 (no)
0 (Event Duration)
"Durer's father died in 1502, and his mother died in 1513."
"What happened after Durer's father died?"
"they snubbed his mother"
0 (no)
1 (Event Ordering)
"Durer's father died in 1502, and his mother died in 1513."
"What happened after Durer's father died?"
"durer's mother became depressed"
1 (yes)
1 (Event Ordering)
"Durer's father died in 1502, and his mother died in 1513."
"What happened after Durer's father died?"
"he got a new job"
0 (no)
1 (Event Ordering)
"Durer's father died in 1502, and his mother died in 1513."
"What happened after Durer's father died?"
"durer's mother died the next minute"
0 (no)
1 (Event Ordering)
"Durer's father died in 1502, and his mother died in 1513."
"What happened after Durer's father died?"
"durer took care of his mother"
1 (yes)
1 (Event Ordering)
"Durer's father died in 1502, and his mother died in 1513."
"Was Durer alive when his mother died?"
"yes"
1 (yes)
4 (Stationarity)
"Durer's father died in 1502, and his mother died in 1513."
"Was Durer alive when his mother died?"
"yes, he was"
1 (yes)
4 (Stationarity)
"Durer's father died in 1502, and his mother died in 1513."
"How often did Durer visit his mother's grave?"
"every hour"
0 (no)
2 (Frequency)
"Durer's father died in 1502, and his mother died in 1513."
"How often did Durer visit his mother's grave?"
"once a minute"
0 (no)
2 (Frequency)
"Durer's father died in 1502, and his mother died in 1513."
"How often did Durer visit his mother's grave?"
"every second"
0 (no)
2 (Frequency)
"Durer's father died in 1502, and his mother died in 1513."
"How often did Durer visit his mother's grave?"
"hourly"
0 (no)
2 (Frequency)
"Durer's father died in 1502, and his mother died in 1513."
"How often did Durer visit his mother's grave?"
"once a century"
0 (no)
2 (Frequency)
"Durer's father died in 1502, and his mother died in 1513."
"How often did Durer visit his mother's grave?"
"every year"
1 (yes)
2 (Frequency)
"Durer's father died in 1502, and his mother died in 1513."
"When did Durer die?"
"40 years later"
1 (yes)
3 (Typical Time)
"Durer's father died in 1502, and his mother died in 1513."
"When did Durer die?"
"360 years later"
0 (no)
3 (Typical Time)
"Durer's father died in 1502, and his mother died in 1513."
"When did Durer die?"
"4545"
0 (no)
3 (Typical Time)
"Durer's father died in 1502, and his mother died in 1513."
"When did Durer die?"
"40 seconds later"
0 (no)
3 (Typical Time)
"Durer's father died in 1502, and his mother died in 1513."
"When did Durer die?"
"April 6, 1528"
1 (yes)
3 (Typical Time)
"Durer's father died in 1502, and his mother died in 1513."
"When did Durer die?"
"170"
0 (no)
3 (Typical Time)
"There were a couple times that the family dog, Mika, has tried to take Joey from Marsha and eat him!"
"How long does it take Mika to try to eat Joey?"
"1 day"
0 (no)
0 (Event Duration)
"There were a couple times that the family dog, Mika, has tried to take Joey from Marsha and eat him!"
"How long does it take Mika to try to eat Joey?"
"0.33 hours"
0 (no)
0 (Event Duration)
"There were a couple times that the family dog, Mika, has tried to take Joey from Marsha and eat him!"
"How long does it take Mika to try to eat Joey?"
"over a year"
0 (no)
0 (Event Duration)
"There were a couple times that the family dog, Mika, has tried to take Joey from Marsha and eat him!"
"How long does it take Mika to try to eat Joey?"
"under an year"
0 (no)
0 (Event Duration)
"There were a couple times that the family dog, Mika, has tried to take Joey from Marsha and eat him!"
"How long does it take Mika to try to eat Joey?"
"3 weeks"
0 (no)
0 (Event Duration)
"There were a couple times that the family dog, Mika, has tried to take Joey from Marsha and eat him!"
"How long does it take Mika to try to eat Joey?"
"over a week"
0 (no)
0 (Event Duration)
"There were a couple times that the family dog, Mika, has tried to take Joey from Marsha and eat him!"
"How long does it take Mika to try to eat Joey?"
"9 hours"
0 (no)
0 (Event Duration)
"There were a couple times that the family dog, Mika, has tried to take Joey from Marsha and eat him!"
"How long does it take Mika to try to eat Joey?"
"over a hour"
0 (no)
0 (Event Duration)
"There were a couple times that the family dog, Mika, has tried to take Joey from Marsha and eat him!"
"How long does it take Mika to try to eat Joey?"
"3 hours"
0 (no)
0 (Event Duration)
"There were a couple times that the family dog, Mika, has tried to take Joey from Marsha and eat him!"
"How long does it take Mika to try to eat Joey?"
"1 century"
0 (no)
0 (Event Duration)
"There were a couple times that the family dog, Mika, has tried to take Joey from Marsha and eat him!"
"What happens after Mika tries to eat Joey?"
"he gets put outside"
1 (yes)
1 (Event Ordering)
"There were a couple times that the family dog, Mika, has tried to take Joey from Marsha and eat him!"
"What happens after Mika tries to eat Joey?"
"joey runs back to mika"
0 (no)
1 (Event Ordering)
"There were a couple times that the family dog, Mika, has tried to take Joey from Marsha and eat him!"
"What happens after Mika tries to eat Joey?"
"mika gets in trouble"
1 (yes)
1 (Event Ordering)
"There were a couple times that the family dog, Mika, has tried to take Joey from Marsha and eat him!"
"What happens after Mika tries to eat Joey?"
"he is praised by marsha"
0 (no)
1 (Event Ordering)
"There were a couple times that the family dog, Mika, has tried to take Joey from Marsha and eat him!"
"What day did Mika try to eat Joey last?"
"wednesday"
1 (yes)
3 (Typical Time)
"There were a couple times that the family dog, Mika, has tried to take Joey from Marsha and eat him!"
"What day did Mika try to eat Joey last?"
"tomorrow"
0 (no)
3 (Typical Time)
"There were a couple times that the family dog, Mika, has tried to take Joey from Marsha and eat him!"
"What day did Mika try to eat Joey last?"
"yesterday"
1 (yes)
3 (Typical Time)
"There were a couple times that the family dog, Mika, has tried to take Joey from Marsha and eat him!"
"What day did Mika try to eat Joey last?"
"today"
1 (yes)
3 (Typical Time)
"So from now on, Marsha takes extra special care to make sure Joey is safe and sound at all times."
"How long had Joey been safe and sound?"
"years"
1 (yes)
0 (Event Duration)
"So from now on, Marsha takes extra special care to make sure Joey is safe and sound at all times."
"How long had Joey been safe and sound?"
"during the past ten months"
1 (yes)
0 (Event Duration)
"So from now on, Marsha takes extra special care to make sure Joey is safe and sound at all times."
"How long had Joey been safe and sound?"
"ever since marsha started taking care of him"
1 (yes)
0 (Event Duration)
"So from now on, Marsha takes extra special care to make sure Joey is safe and sound at all times."
"How long had Joey been safe and sound?"
"3 minute"
0 (no)
0 (Event Duration)
"So from now on, Marsha takes extra special care to make sure Joey is safe and sound at all times."
"How long had Joey been safe and sound?"
"90 years"
0 (no)
0 (Event Duration)
"So from now on, Marsha takes extra special care to make sure Joey is safe and sound at all times."
"What happened before Martha took extra care?"
"you make sure the president takes the responsibility"
0 (no)
1 (Event Ordering)
"So from now on, Marsha takes extra special care to make sure Joey is safe and sound at all times."
"What happened before Martha took extra care?"
"joey died"
0 (no)
1 (Event Ordering)
"So from now on, Marsha takes extra special care to make sure Joey is safe and sound at all times."
"Is Joey safe and sound today?"
"yes"
1 (yes)
4 (Stationarity)
"So from now on, Marsha takes extra special care to make sure Joey is safe and sound at all times."
"How often was Joey safe and sound?"
"once in a month"
0 (no)
2 (Frequency)
"So from now on, Marsha takes extra special care to make sure Joey is safe and sound at all times."
"How often was Joey safe and sound?"
"once a hour"
0 (no)
2 (Frequency)
"So from now on, Marsha takes extra special care to make sure Joey is safe and sound at all times."
"How often was Joey safe and sound?"
"once in a minute"
0 (no)
2 (Frequency)
"So from now on, Marsha takes extra special care to make sure Joey is safe and sound at all times."
"How often was Joey safe and sound?"
"once a second"
0 (no)
2 (Frequency)
"So from now on, Marsha takes extra special care to make sure Joey is safe and sound at all times."
"How often was Joey safe and sound?"
"once a year"
0 (no)
2 (Frequency)
"So from now on, Marsha takes extra special care to make sure Joey is safe and sound at all times."
"How often was Joey safe and sound?"
"every day"
1 (yes)
2 (Frequency)
"So from now on, Marsha takes extra special care to make sure Joey is safe and sound at all times."
"How often was Joey safe and sound?"
"once a week"
0 (no)
2 (Frequency)
"So from now on, Marsha takes extra special care to make sure Joey is safe and sound at all times."
"How often was Joey safe and sound?"
"every century"
0 (no)
2 (Frequency)
"So from now on, Marsha takes extra special care to make sure Joey is safe and sound at all times."
"How often was Joey safe and sound?"
"once in a day"
0 (no)
2 (Frequency)
"So from now on, Marsha takes extra special care to make sure Joey is safe and sound at all times."
"How often was Joey safe and sound?"
"once in a century"
0 (no)
2 (Frequency)
"So from now on, Marsha takes extra special care to make sure Joey is safe and sound at all times."
"How often was Joey safe and sound?"
"never"
0 (no)
2 (Frequency)
"So from now on, Marsha takes extra special care to make sure Joey is safe and sound at all times."
"How often was Joey safe and sound?"
"always"
1 (yes)
2 (Frequency)
"So from now on, Marsha takes extra special care to make sure Joey is safe and sound at all times."
"How often was Joey safe and sound?"
"at all times"
1 (yes)
2 (Frequency)
"So from now on, Marsha takes extra special care to make sure Joey is safe and sound at all times."
"How often was Joey safe and sound?"
"sometimes"
0 (no)
2 (Frequency)
"So from now on, Marsha takes extra special care to make sure Joey is safe and sound at all times."
"What day did Martha decide to take extra care?"
"you make sure the president takes the responsibility"
0 (no)
3 (Typical Time)
"So from now on, Marsha takes extra special care to make sure Joey is safe and sound at all times."
"What day did Martha decide to take extra care?"
"every century"
0 (no)
3 (Typical Time)
"So from now on, Marsha takes extra special care to make sure Joey is safe and sound at all times."
"What day did Martha decide to take extra care?"
0 (no)
3 (Typical Time)
"So from now on, Marsha takes extra special care to make sure Joey is safe and sound at all times."
"What day did Martha decide to take extra care?"
"wednesday"
1 (yes)
3 (Typical Time)
"So from now on, Marsha takes extra special care to make sure Joey is safe and sound at all times."
"What day did Martha decide to take extra care?"
"to make standard pencils takes five steps"
0 (no)
3 (Typical Time)
"So from now on, Marsha takes extra special care to make sure Joey is safe and sound at all times."
"What day did Martha decide to take extra care?"
"monday"
1 (yes)
3 (Typical Time)
"So from now on, Marsha takes extra special care to make sure Joey is safe and sound at all times."
"What day did Martha decide to take extra care?"
"friday"
1 (yes)
3 (Typical Time)
"So from now on, Marsha takes extra special care to make sure Joey is safe and sound at all times."
"What day did Martha decide to take extra care?"
"every month"
0 (no)
3 (Typical Time)
"So from now on, Marsha takes extra special care to make sure Joey is safe and sound at all times."
"What day did Martha decide to take extra care?"
"every minute"
0 (no)
3 (Typical Time)
"He spoke the last word with such heavy intonation that Allan shrank back before the physical wave of sound emanating from Arthur's throat."
"How often does Arthur talk to Allan?"
"each hour"
0 (no)
2 (Frequency)
"He spoke the last word with such heavy intonation that Allan shrank back before the physical wave of sound emanating from Arthur's throat."
"How often does Arthur talk to Allan?"
"every hour"
0 (no)
2 (Frequency)
"He spoke the last word with such heavy intonation that Allan shrank back before the physical wave of sound emanating from Arthur's throat."
"How often does Arthur talk to Allan?"
"every year"
0 (no)
2 (Frequency)
"He spoke the last word with such heavy intonation that Allan shrank back before the physical wave of sound emanating from Arthur's throat."
"How often does Arthur talk to Allan?"
"every day"
1 (yes)
2 (Frequency)
"He spoke the last word with such heavy intonation that Allan shrank back before the physical wave of sound emanating from Arthur's throat."
"How often does Arthur talk to Allan?"
"each day"
1 (yes)
2 (Frequency)
"He spoke the last word with such heavy intonation that Allan shrank back before the physical wave of sound emanating from Arthur's throat."
"How often does Arthur talk to Allan?"
"daily"
1 (yes)
2 (Frequency)
"He spoke the last word with such heavy intonation that Allan shrank back before the physical wave of sound emanating from Arthur's throat."
"How often does Arthur talk to Allan?"
"every morning"
1 (yes)
2 (Frequency)
"He spoke the last word with such heavy intonation that Allan shrank back before the physical wave of sound emanating from Arthur's throat."
"How often does Arthur talk to Allan?"
"every second"
0 (no)
2 (Frequency)
"He spoke the last word with such heavy intonation that Allan shrank back before the physical wave of sound emanating from Arthur's throat."
"How often does Arthur talk to Allan?"
"all day"
0 (no)
2 (Frequency)
"He spoke the last word with such heavy intonation that Allan shrank back before the physical wave of sound emanating from Arthur's throat."
"How often does Arthur talk to Allan?"
"every minute"
0 (no)
2 (Frequency)
"He spoke the last word with such heavy intonation that Allan shrank back before the physical wave of sound emanating from Arthur's throat."
"How often does Arthur talk to Allan?"
"every century"
0 (no)
2 (Frequency)
"He spoke the last word with such heavy intonation that Allan shrank back before the physical wave of sound emanating from Arthur's throat."
"What did Arthur do after talking to Allan?"
"he laughed"
0 (no)
1 (Event Ordering)
"He spoke the last word with such heavy intonation that Allan shrank back before the physical wave of sound emanating from Arthur's throat."
"What did Arthur do after talking to Allan?"
"talk to him again"
0 (no)
1 (Event Ordering)
"He spoke the last word with such heavy intonation that Allan shrank back before the physical wave of sound emanating from Arthur's throat."
"What did Arthur do after talking to Allan?"
"he laughs"
0 (no)
1 (Event Ordering)
"He spoke the last word with such heavy intonation that Allan shrank back before the physical wave of sound emanating from Arthur's throat."
"What did Arthur do after talking to Allan?"
"left"
1 (yes)
1 (Event Ordering)
"These small, dark Melanesians are related in type to Australian aborigines and are confined today to the forests of the northern highlands."
"How long have they been confined to the forests?"
"70 years"
1 (yes)
0 (Event Duration)
"These small, dark Melanesians are related in type to Australian aborigines and are confined today to the forests of the northern highlands."
"How long have they been confined to the forests?"
"30 centuries"
0 (no)
0 (Event Duration)
"These small, dark Melanesians are related in type to Australian aborigines and are confined today to the forests of the northern highlands."
"Will they be related to Australian aborigines when they leave the forest?"
"yes"
1 (yes)
4 (Stationarity)
"These small, dark Melanesians are related in type to Australian aborigines and are confined today to the forests of the northern highlands."
"Will they be related to Australian aborigines when they leave the forest?"
"however they are a new species"
0 (no)
4 (Stationarity)
End of preview (truncated to 100 rows)

# Dataset Card for MC-TACO

### 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.

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?",
"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 (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).

### 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.

## Considerations for Using the Data

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 for adding this dataset.