COVIDRead: A Large-scale Question Answering Dataset on COVID-19
By Tanik Saikh, Sovan Kumar Sahoo, Asif Ekbal, Pushpak Bhattacharyya. 2021
6 citations recorded by the source corpus.
During this pandemic situation, extracting any relevant information related to COVID-19 will be immensely beneficial to the community at large. In this paper, we present a very important resource, COVIDRead, a Stanford Question Answering Dataset (SQuAD) like dataset over more than 100k question-answer pairs. The dataset consists of Context-Answer-Question triples. Primarily the questions from the context are constructed in an automated way. After that, the system-generated questions are manually checked by hu-mans annotators. This is a precious resource that could serve many purposes, ranging from common people queries regarding this very uncommon disease to managing articles by editors/associate editors of a journal. We establish several end-to-end neural network based baseline models that attain the lowest F1 of 32.03% and the highest F1 of 37.19%. To the best of our knowledge, we are the first to provide this kind of QA dataset in such a large volume on COVID-19. This dataset creates a new avenue of carrying out research on COVID-19 by providing a benchmark dataset and a baseline model.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Information Retrieval & Search, Natural Language Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Sebastian Riedel
- Wen-tau Yih
- Patrick Lewis
- Fabio Petroni
- Hannaneh Hajishirzi
- Luke S. Zettlemoyer
- Gautier Izacard
- Danqi Chen
In Computer Vision and Pattern Recognition
- Sebastian Riedel
- Patrick Lewis
- Wen-tau Yih
- Fabio Petroni
- Gautier Izacard
- Hannaneh Hajishirzi
- Aleksandra Piktus
- Edouard Grave
In Information Retrieval & Search
- Sebastian Riedel
- Wen-tau Yih
- Fabio Petroni
- Gautier Izacard
- Patrick Lewis
- Edouard Grave
- Aleksandra Piktus
- Dmytro Okhonko
In Natural Language Processing
- Sebastian Riedel
- Patrick Lewis
- Fabio Petroni
- Wen-tau Yih
- Gautier Izacard
- Hannaneh Hajishirzi
- Edouard Grave
- Luke S. Zettlemoyer
Citations in the corpus
- The Web Is Your Oyster - Knowledge-Intensive NLP against a Very Large Web Corpus (2021, background): “…be considered to tackle KI tasks, such as large collections of question-answer pairs (Lewis et al., 2021; Huber et al., 2021), structured knowledge sources (Berant et al., 2013; Levy et al., 2017; Elsahar et al., 2018) or domain specific collections (Tsatsaronis et al., 2015; Saikh et al., 2021).”