ABSTRACT
The COVID-19 outbreak has heightened the need for systems that enable information seekers to search vast corpora of scientific articles to find answers to their natural language questions. This paper describes the participation of the U.S. National Library of Medicine (NLM) team in BioASQ Task Synergy on biomedical semantic question answering for COVID-19. In this work, we exploited the pre-trained Transformer models such as T5 and BART for document re-ranking, passage retrieval, and answer generation. Official results show that among the participating systems, our models achieve strong performance in document retrieval, passage retrieval, and the “ideal answer” generation task. © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
ABSTRACT
This paper presents the ideas for the 2021 ImageCLEF lab that will be organized as part of the Conference and Labs of the Evaluation Forum—CLEF Labs 2021 in Bucharest, Romania. ImageCLEF is an ongoing evaluation initiative (active since 2003) that promotes the evaluation of technologies for annotation, indexing and retrieval of visual data with the aim of providing information access to large collections of images in various usage scenarios and domains. In 2021, the 19th edition of ImageCLEF will organize four main tasks: (i) a Medical task addressing visual question answering, a concept annotation and a tuberculosis classification task, (ii) a Coral task addressing the annotation and localisation of substrates in coral reef images, (iii) a DrawnUI task addressing the creation of websites from either a drawing or a screenshot by detecting the different elements present on the design and a new (iv) Aware task addressing the prediction of real-life consequences of online photo sharing. The strong participation in 2020, despite the COVID pandemic, with over 115 research groups registering and 40 submitting over 295 runs for the tasks shows an important interest in this benchmarking campaign. We expect the new tasks to attract at least as many researchers for 2021. © 2021, Springer Nature Switzerland AG.