Datasets:
Tasks:
Question Answering
Languages:
English
Multilinguality:
monolingual
Size Categories:
1K<n<10K
Language Creators:
found
Annotations Creators:
expert-generated
Source Datasets:
original
ArXiv:
Tags:
License:
annotations_creators: | |
- expert-generated | |
language_creators: | |
- found | |
language: | |
- en | |
license: | |
- apache-2.0 | |
multilinguality: | |
- monolingual | |
size_categories: | |
- 1K<n<10K | |
source_datasets: | |
- original | |
task_categories: | |
- question-answering | |
task_ids: | |
- open-domain-qa | |
- extractive-qa | |
paperswithcode_id: covidqa | |
pretty_name: CovidQaCastorini | |
dataset_info: | |
- config_name: covid_qa_deepset | |
features: | |
- name: document_id | |
dtype: int32 | |
- name: context | |
dtype: string | |
- name: question | |
dtype: string | |
- name: is_impossible | |
dtype: bool | |
- name: id | |
dtype: int32 | |
- name: answers | |
sequence: | |
- name: text | |
dtype: string | |
- name: answer_start | |
dtype: int32 | |
splits: | |
- name: train | |
num_bytes: 65151262 | |
num_examples: 2019 | |
download_size: 4418117 | |
dataset_size: 65151262 | |
- config_name: covidqa | |
features: | |
- name: category_name | |
dtype: string | |
- name: question_query | |
dtype: string | |
- name: keyword_query | |
dtype: string | |
- name: answers | |
sequence: | |
- name: id | |
dtype: string | |
- name: title | |
dtype: string | |
- name: exact_answer | |
dtype: string | |
splits: | |
- name: train | |
num_bytes: 33757 | |
num_examples: 27 | |
download_size: 51438 | |
dataset_size: 33757 | |
- config_name: covid_qa_castorini | |
features: | |
- name: category_name | |
dtype: string | |
- name: question_query | |
dtype: string | |
- name: keyword_query | |
dtype: string | |
- name: answers | |
sequence: | |
- name: id | |
dtype: string | |
- name: title | |
dtype: string | |
- name: exact_answer | |
dtype: string | |
splits: | |
- name: train | |
num_bytes: 33757 | |
num_examples: 27 | |
download_size: 51438 | |
dataset_size: 33757 | |
# Dataset Card for [covid_qa_castorini] | |
## 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:** http://covidqa.ai | |
- **Repository:** https://github.com/castorini/pygaggle | |
- **Paper:** https://arxiv.org/abs/2004.11339 | |
- **Point of Contact:** [Castorini research group @UWaterloo](https://github.com/castorini/) | |
### Dataset Summary | |
CovidQA is a question answering dataset specifically designed for COVID-19, built by hand from knowledge gathered | |
from Kaggle’s COVID-19 Open Research Dataset Challenge. | |
The dataset comprises 156 question-article pairs with 27 questions (topics) and 85 unique articles. | |
### Supported Tasks and Leaderboards | |
[More Information Needed] | |
### Languages | |
The text in the dataset is in English. | |
## Dataset Structure | |
### Data Instances | |
**What do the instances that comprise the dataset represent?** | |
Each represents a question, a context (document passage from the CORD19 dataset) and an answer. | |
**How many instances are there in total?** | |
**What data does each instance consist of?** | |
Each instance is a query (natural language question and keyword-based), a set of answers, and a document id with its title associated with each answer. | |
[More Information Needed] | |
### Data Fields | |
The data was annotated in SQuAD style fashion, where each row contains: | |
* **question_query**: Natural language question query | |
* **keyword_query**: Keyword-based query | |
* **category_name**: Category in which the queries are part of | |
* **answers**: List of answers | |
* **id**: The document ID the answer is found on | |
* **title**: Title of the document of the answer | |
* **exact_answer**: Text (string) of the exact answer | |
### Data Splits | |
**data/kaggle-lit-review-0.2.json**: 156 question-article pairs with 27 questions (topics) and 85 unique articles from | |
CORD-19. | |
[More Information Needed] | |
## Dataset Creation | |
The dataset aims to help for guiding research until more substantial evaluation resources become available. Being a smaller dataset, | |
it can be helpful for evaluating the zero-shot or transfer capabilities of existing models on topics specifically related to COVID-19. | |
### Curation Rationale | |
[More Information Needed] | |
### Source Data | |
#### Initial Data Collection and Normalization | |
#### Who are the source language producers? | |
[More Information Needed] | |
### Annotations | |
Five of the co-authors participated in this annotation effort, applying the aforementioned approach, with one lead | |
annotator responsible for approving topics and answering technical questions from the other annotators. Two annotators are | |
undergraduate students majoring in computer science, one is a science alumna, another is a computer science professor, | |
and the lead annotator is a graduate student in computer science—all affiliated with the University of Waterloo. | |
#### Annotation process | |
#### Who are the annotators? | |
### Personal and Sensitive Information | |
[More Information Needed] | |
## Considerations for Using the Data | |
### Social Impact of Dataset | |
The dataset was intended as a stopgap measure for guiding research until more substantial evaluation resources become available. | |
### Discussion of Biases | |
[More Information Needed] | |
### Other Known Limitations | |
While this dataset, comprising 124 question–article pairs as of the present version 0.1 release, does not have sufficient | |
examples for supervised machine learning, it can be helpful for evaluating the zero-shot or transfer capabilities | |
of existing models on topics specifically related to COVID-19. | |
## Additional Information | |
The listed authors in the homepage are maintaining/supporting the dataset. | |
### Dataset Curators | |
[More Information Needed] | |
### Licensing Information | |
The dataset is licensed under the [Apache License 2.0](https://github.com/castorini/pygaggle/blob/master/LICENSE). | |
### Citation Information | |
``` | |
@article{tang2020rapidly, | |
title={Rapidly Bootstrapping a Question Answering Dataset for COVID-19}, | |
author={Tang, Raphael and Nogueira, Rodrigo and Zhang, Edwin and Gupta, Nikhil and Cam, Phuong and Cho, Kyunghyun and Lin, Jimmy}, | |
journal={arXiv preprint arXiv:2004.11339}, | |
year={2020} | |
} | |
``` | |
### Contributions | |
Thanks to [@olinguyen](https://github.com/olinguyen) for adding this dataset. |