ViQA-COVID: COVID-19 Machine Reading Comprehension Dataset for Vietnamese

• URL: http://arxiv.org/abs/2504.21017v2
• Date: Sat, 14 Jun 2025 11:04:49 GMT
• Title: ViQA-COVID: COVID-19 Machine Reading Comprehension Dataset for Vietnamese
• Authors: Hai-Chung Nguyen-Phung, Ngoc C. Lê, Van-Chien Nguyen, Hang Thi Nguyen, Thuy Phuong Thi Nguyen,
• Abstract summary: ViQA-COVID is the first MRC dataset about COVID-19 for Vietnamese.<n>It can be used to build models and systems, contributing to disease prevention.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: After two years of appearance, COVID-19 has negatively affected people and normal life around the world. As in May 2022, there are more than 522 million cases and six million deaths worldwide (including nearly ten million cases and over forty-three thousand deaths in Vietnam). Economy and society are both severely affected. The variant of COVID-19, Omicron, has broken disease prevention measures of countries and rapidly increased number of infections. Resources overloading in treatment and epidemics prevention is happening all over the world. It can be seen that, application of artificial intelligence (AI) to support people at this time is extremely necessary. There have been many studies applying AI to prevent COVID-19 which are extremely useful, and studies on machine reading comprehension (MRC) are also in it. Realizing that, we created the first MRC dataset about COVID-19 for Vietnamese: ViQA-COVID and can be used to build models and systems, contributing to disease prevention. Besides, ViQA-COVID is also the first multi-span extraction MRC dataset for Vietnamese, we hope that it can contribute to promoting MRC studies in Vietnamese and multilingual.

Related papers

• Nested Named-Entity Recognition on Vietnamese COVID-19: Dataset and Experiments [0.8803472017068046]
  We describe a named-entity recognition (NER) study that assists in the prevention of COVID-19 pandemic in Vietnam.<n>We also present our manually annotated COVID-19 dataset with nested named entity recognition task for Vietnamese.
  arXiv  Detail & Related papers  (2025-04-21T05:21:34Z)
• COVID-19 Spreading Prediction and Impact Analysis by Using Artificial Intelligence for Sustainable Global Health Assessment [0.0]
  The current epidemic of COVID-19 has influenced more than 2,164,111 persons and killed more than 146,198 folks in over 200 countries across the globe. The fundamental difficulties of AI in this situation is the limited availability of information and the uncertain nature of the disease. Here in this article, we have tried to integrate AI to predict the infection outbreak and along with this, we have also tried to test whether AI with help deep learning can recognize COVID-19 infected chest X-Rays or not.
  arXiv  Detail & Related papers  (2023-04-23T19:48:29Z)
• Human Behavior in the Time of COVID-19: Learning from Big Data [71.26355067309193]
  Since March 2020, there have been over 600 million confirmed cases of COVID-19 and more than six million deaths. The pandemic has impacted and even changed human behavior in almost every aspect. Researchers have been employing big data techniques such as natural language processing, computer vision, audio signal processing, frequent pattern mining, and machine learning.
  arXiv  Detail & Related papers  (2023-03-23T17:19:26Z)
• UIT-ViCoV19QA: A Dataset for COVID-19 Community-based Question Answering on Vietnamese Language [0.0]
  We present the first Vietnamese community-based question answering dataset for developing question answering systems for COVID-19 called UIT-ViCoV19QA. The dataset comprises 4,500 question-answer pairs collected from trusted medical sources, with at least one answer and at most four unique paraphrased answers per question.
  arXiv  Detail & Related papers  (2022-09-14T14:24:23Z)
• Artificial Intelligence (AI) and Big Data for Coronavirus (COVID-19) Pandemic: A Survey on the State-of-the-Arts [10.741018907229927]
  The very first infected novel coronavirus case (COVID-19) was found in Hubei, China in Dec. 2019. The COVID-19 pandemic has spread over 214 countries and areas in the world, and has significantly affected every aspect of our daily lives. Motivated by recent advances and applications of artificial intelligence (AI) and big data in various areas, this paper aims at emphasizing their importance in responding to the COVID-19 outbreak.
  arXiv  Detail & Related papers  (2021-07-17T13:12:30Z)
• The illicit trade of COVID-19 vaccines on the dark web [55.45786602961871]
  Early analyses revealed that dark web marketplaces (DWMs) started offering COVID-19 related products (e.g., masks and COVID-19 tests) as soon as the COVID-19 pandemic started. Here, we broaden the scope and depth of previous investigations by analysing 194 DWMs until July 2021, including the crucial period in which vaccines became available. We show that recreational drugs are the most affected among traditional DWMs product, with COVID-19 mentions steadily increasing since March 2020.
  arXiv  Detail & Related papers  (2021-02-10T14:52:54Z)
• COVID-19 Pandemic Outbreak in the Subcontinent: A data-driven analysis [0.8057708414390126]
  COVID-19 virus emerged in late December 2019 in Wuhan city, Hubei, China. Numerous studies claim that the subcontinent could remain in the worst affected region by the COVID-19. This paper uses publicly available epidemiological data of Bangladesh, India, and Pakistan to estimate the reproduction numbers.
  arXiv  Detail & Related papers  (2020-08-22T10:40:17Z)
• Understanding the temporal evolution of COVID-19 research through machine learning and natural language processing [66.63200823918429]
  The outbreak of the novel coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been continuously affecting human lives and communities around the world. We used multiple data sources, i.e., PubMed and ArXiv, and built several machine learning models to characterize the landscape of current COVID-19 research. Our findings confirm the types of research available in PubMed and ArXiv differ significantly, with the former exhibiting greater diversity in terms of COVID-19 related issues.
  arXiv  Detail & Related papers  (2020-07-22T18:02:39Z)
• A Survey on Applications of Artificial Intelligence in Fighting Against COVID-19 [75.84689958489724]
  The COVID-19 pandemic caused by the SARS-CoV-2 virus has spread rapidly worldwide, leading to a global outbreak. As a powerful tool against COVID-19, artificial intelligence (AI) technologies are widely used in combating this pandemic. This survey presents medical and AI researchers with a comprehensive view of the existing and potential applications of AI technology in combating COVID-19.
  arXiv  Detail & Related papers  (2020-07-04T22:48:15Z)
• Cross-lingual Transfer Learning for COVID-19 Outbreak Alignment [90.12602012910465]
  We train on Italy's early COVID-19 outbreak through Twitter and transfer to several other countries. Our experiments show strong results with up to 0.85 Spearman correlation in cross-country predictions.
  arXiv  Detail & Related papers  (2020-06-05T02:04:25Z)
• Mapping the Landscape of Artificial Intelligence Applications against COVID-19 [59.30734371401316]
  COVID-19, the disease caused by the SARS-CoV-2 virus, has been declared a pandemic by the World Health Organization. We present an overview of recent studies using Machine Learning and, more broadly, Artificial Intelligence to tackle many aspects of the COVID-19 crisis.
  arXiv  Detail & Related papers  (2020-03-25T12:30:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.