# Participation in TREC 2020 COVID Track Using Continuous Active Learning

arXiv: https://arxiv.org/abs/2011.01453 · HTML: https://mlsanity.com/paper/2011.01453/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Nov 2020.

## Built on by (most cited first, 2 of 2)

- [Goldilocks: Just-Right Tuning of BERT for Technology-Assisted Review](https://mlsanity.com/paper/2105.01044/) (2021)
- [Establishing Strong Baselines for TripClick Health Retrieval](https://mlsanity.com/paper/2201.00365/) (2022)

## Abstract (arXiv)

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.