Datasets:
Tasks:
Question Answering
Sub-tasks:
multiple-choice-qa
Languages:
English
Size:
10K<n<100K
ArXiv:
License:
| 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. |