# Dataset: mc_taco

Languages: en
Multilinguality: monolingual
Size Categories: 10K<n<100K
Language Creators: crowdsourced found
Source Datasets: original

# Dataset Card Creation Guide

### 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.

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