Datasets:
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:
- closed-domain-qa
- extractive-qa
paperswithcode_id: null
pretty_name: COVID-QA
dataset_info:
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
config_name: covid_qa_deepset
splits:
- name: train
num_bytes: 65151262
num_examples: 2019
download_size: 4418117
dataset_size: 65151262
Dataset Card for COVID-QA
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Repository: https://github.com/deepset-ai/COVID-QA
- Paper: https://openreview.net/forum?id=JENSKEEzsoU
- Point of Contact: deepset AI
Dataset Summary
COVID-QA is a Question Answering dataset consisting of 2,019 question/answer pairs annotated by volunteer biomedical experts on scientific articles related to COVID-19. A total of 147 scientific articles from the CORD-19 dataset were annotated by 15 experts.
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? 2019 instances
What data does each instance consist of? Each instance is a question, a set of answers, and an id associated with each answer.
[More Information Needed]
Data Fields
The data was annotated in SQuAD style fashion, where each row contains:
- question: Query question
- context: Context text to obtain the answer from
- document_id The document ID of the context text
- answer: Dictionary containing the answer string and the start index
Data Splits
data/COVID-QA.json: 2,019 question/answer pairs annotated by volunteer biomedical experts on scientific articles related to COVID-19.
[More Information Needed]
Dataset Creation
Curation Rationale
[More Information Needed]
Source Data
Initial Data Collection and Normalization
The inital data collected comes from 147 scientific articles from the CORD-19 dataset. Question and answers were then annotated afterwards.
Who are the source language producers?
[More Information Needed]
Annotations
Annotation process
While annotators were volunteers, they were required to have at least a Master’s degree in biomedical sciences. The annotation team was led by a medical doctor (G.A.R.) who vetted the volunteer’s credentials and manually verified each question/answer pair produced. We used an existing, web-based annotation tool that had been created by deepset and is available at their Neural Search framework haystack.
Who are the annotators?
The annotators are 15 volunteer biomedical experts on scientific articles related to COVID-19.
Personal and Sensitive Information
[More Information Needed]
Considerations for Using the Data
Social Impact of Dataset
The dataset aims to help build question answering models serving clinical and scientific researchers, public health authorities, and frontline workers. These QA systems can help them find answers and patterns in research papers by locating relevant answers to common questions from scientific articles.
Discussion of Biases
[More Information Needed]
Other Known Limitations
Additional Information
The listed authors in the homepage are maintaining/supporting the dataset.
Dataset Curators
[More Information Needed]
Licensing Information
The Proto_qa dataset is licensed under the Apache License 2.0
Citation Information
@inproceedings{moller2020covid,
title={COVID-QA: A Question Answering Dataset for COVID-19},
author={M{\"o}ller, Timo and Reina, Anthony and Jayakumar, Raghavan and Pietsch, Malte},
booktitle={Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020},
year={2020}
}
Contributions
Thanks to @olinguyen for adding this dataset.