论文标题
Sledge-Z：Covid-19文献搜索的零射基线
SLEDGE-Z: A Zero-Shot Baseline for COVID-19 Literature Search
论文作者
论文摘要
围绕严重急性呼吸综合症2（SARS-COV-2）的全球担忧，有关该病毒的科学文献迅速增长。临床医生，研究人员和决策者需要能够有效地搜索这些文章。在这项工作中，我们提出了一种适应与共同相关的科学文献的零击排名算法。我们的方法将培训数据从另一个集合到医疗相关的查询过滤，使用对科学文本（SCIBERT）预先训练的神经重新排列模型，并过滤目标文档收集。该方法在TREC Covid Covid 1排行榜上排名最高，并在第1轮和2轮判断上进行评估时，在0.80的P@5和0.68的NDCG@10中排名最高。尽管不依赖Trec-Covid数据，但我们的方法优于使用的模型。作为彻底评估Covid-19搜索的首批搜索方法之一，我们希望这是强大的基准，并有助于全球危机。
With worldwide concerns surrounding the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), there is a rapidly growing body of scientific literature on the virus. Clinicians, researchers, and policy-makers need to be able to search these articles effectively. In this work, we present a zero-shot ranking algorithm that adapts to COVID-related scientific literature. Our approach filters training data from another collection down to medical-related queries, uses a neural re-ranking model pre-trained on scientific text (SciBERT), and filters the target document collection. This approach ranks top among zero-shot methods on the TREC COVID Round 1 leaderboard, and exhibits a P@5 of 0.80 and an nDCG@10 of 0.68 when evaluated on both Round 1 and 2 judgments. Despite not relying on TREC-COVID data, our method outperforms models that do. As one of the first search methods to thoroughly evaluate COVID-19 search, we hope that this serves as a strong baseline and helps in the global crisis.
