论文标题
使用连续的主动学习参与TREC 2020 COVID轨道
Participation in TREC 2020 COVID Track Using Continuous Active Learning
论文作者
论文摘要
我们描述了我们参与TREC 2020 Covid Track（TREC-COVID）的所有五轮比赛。 Trec-Covid的目的是通过确定许多紧迫的问题和建立基础设施以改善搜索系统的答案来为对Covid-19的大流行的反应做出贡献[8]。这首轨道的所有五轮比赛都挑战参与者在新数据收集Cord-19上执行经典的临时搜索任务。我们的解决方案通过应用持续的主动学习模型（CAL）及其变化来应对这一挑战。我们的结果表明我们将成为评分手册的最高运行之一，并且我们在所有类别的提交类别中保持竞争力。
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.
