论文标题
COVIDEX：COVID-19开放研究数据集的神经排名模型和关键字搜索基础架构
Covidex: Neural Ranking Models and Keyword Search Infrastructure for the COVID-19 Open Research Dataset
论文作者
论文摘要
我们提出了Covidex，这是一种利用最新神经排名模型的搜索引擎，以提供对Allen AI研究所策划的COVID-19开放研究数据集的信息访问。自2020年3月下旬以来，我们的系统一直在线并为用户提供服务。Covidex是我们三支策略的用户应用程序组成部分，以开发帮助领域专家解决持续的全球大流行的技术。此外，我们还提供了可靠且易于使用的关键字搜索基础架构，以利用基于成熟的融合方法以及可以纳入其他应用程序的独立神经排名模型。这些技术已经在正在进行的TREC循环挑战中进行了评估：许多参与者已经采用了我们的基础设施和基准，包括在第1、2和3轮中得分最高的一些跑步。在第3轮中，我们报告了得分最高的跑步，它利用了以前的训练数据，第二高的训练数据和第二高的全自动跑步。
We present Covidex, a search engine that exploits the latest neural ranking models to provide information access to the COVID-19 Open Research Dataset curated by the Allen Institute for AI. Our system has been online and serving users since late March 2020. The Covidex is the user application component of our three-pronged strategy to develop technologies for helping domain experts tackle the ongoing global pandemic. In addition, we provide robust and easy-to-use keyword search infrastructure that exploits mature fusion-based methods as well as standalone neural ranking models that can be incorporated into other applications. These techniques have been evaluated in the ongoing TREC-COVID challenge: Our infrastructure and baselines have been adopted by many participants, including some of the highest-scoring runs in rounds 1, 2, and 3. In round 3, we report the highest-scoring run that takes advantage of previous training data and the second-highest fully automatic run.
