Participation in TREC 2020 COVID Track Using Continuous Active Learning
This work addresses the need for improved search systems to identify answers to COVID-19-related questions, though it is incremental as it applies an existing method to a new dataset.
The paper tackled the TREC 2020 COVID Track ad-hoc search task on the CORD-19 dataset by applying the Continuous Active Learning (CAL) model and its variations, achieving top scores among manual runs and remaining competitive across all submission categories.
We describe our participation in all five rounds of the TREC 2020 COVID Track (TREC-COVID). The goal of TREC-COVID is to contribute to the response to the COVID-19 pandemic by identifying answers to many pressing questions and building infrastructure to improve search systems [8]. All five rounds of this Track challenged participants to perform a classic ad-hoc search task on the new data collection CORD-19. Our solution addressed this challenge by applying the Continuous Active Learning model (CAL) and its variations. Our results showed us to be amongst the top scoring manual runs and we remained competitive within all categories of submissions.