论文标题
重新利用TREC-COVID注释来回答CORD-19的关键问题
Repurposing TREC-COVID Annotations to Answer the Key Questions of CORD-19
论文作者
论文摘要
2019年新型冠状病毒病（Covid-19）于2019年底在中国武汉开始，迄今为止已感染了全球1400万以上人，导致750,000多人死亡。 2020年3月10日，世界卫生组织（WHO）宣布爆发是全球流行病。许多不限于医疗领域的学者和研究人员开始发表描述新发现的论文。但是，随着大量出版物的大量涌入，这些人很难筛选大量数据并理解这些发现。白宫和一组行业研究实验室，由艾伦AI研究所领导，汇总了200,000多种与各种冠状病毒有关的期刊文章，并要求社区回答与语料库有关的关键问题，并将数据集释放为Cord-19。信息检索（IR）社区重新使用了Cord-19中的期刊文章，以更类似于经典的TREC风格的竞争，称为TREC-COVID，人类注释者在每轮比赛结束时提供了相关判断。看到相关的努力，我们着手重新利用Trec-Covid任务的相关注释，以识别Cord-19中的期刊文章，这些文章与Cord-19的关键问题有关。对此重新使用的数据集进行了培训的生物Biobert模型，规定了CORD-19任务的相关注释，该任务与Cohen的Kappa有关，与大多数人类注释达到了总数为0.4430。我们介绍用于构建新数据集的方法并描述整个过程中使用的决策过程。
The novel coronavirus disease 2019 (COVID-19) began in Wuhan, China in late 2019 and to date has infected over 14M people worldwide, resulting in over 750,000 deaths. On March 10, 2020 the World Health Organization (WHO) declared the outbreak a global pandemic. Many academics and researchers, not restricted to the medical domain, began publishing papers describing new discoveries. However, with the large influx of publications, it was hard for these individuals to sift through the large amount of data and make sense of the findings. The White House and a group of industry research labs, lead by the Allen Institute for AI, aggregated over 200,000 journal articles related to a variety of coronaviruses and tasked the community with answering key questions related to the corpus, releasing the dataset as CORD-19. The information retrieval (IR) community repurposed the journal articles within CORD-19 to more closely resemble a classic TREC-style competition, dubbed TREC-COVID, with human annotators providing relevancy judgements at the end of each round of competition. Seeing the related endeavors, we set out to repurpose the relevancy annotations for TREC-COVID tasks to identify journal articles in CORD-19 which are relevant to the key questions posed by CORD-19. A BioBERT model trained on this repurposed dataset prescribes relevancy annotations for CORD-19 tasks that have an overall agreement of 0.4430 with majority human annotations in terms of Cohen's kappa. We present the methodology used to construct the new dataset and describe the decision process used throughout.
