We introduce BIOMRC, a large-scale cloze-style biomedical MRC dataset. Care
was taken to reduce noise, compared to the previous BIOREAD dataset of Pappas
et al. (2018). Experiments show that simple heuristics do not perform well on
the new dataset, and that two neural MRC models that had been tested on BIOREAD
perform much better on BIOMRC, indicating that the new dataset is indeed less
noisy or at least that its task is more feasible. Non-expert human performance
is also higher on the new dataset compared to BIOREAD, and biomedical experts
perform even better. We also introduce a new BERT-based MRC model, the best
version of which substantially outperforms all other methods tested, reaching
or surpassing the accuracy of biomedical experts in some experiments. We make
the new dataset available in three different sizes, also releasing our code,
and providing a leaderboard.

介绍了 BIOMRC 大规模填空式生物医学 MRC 数据集及相关实验结果，新数据集噪声更小，任务也更可行，比以前的 BIOREAD 数据集表现更好。同时，介绍了一个基于 BERT 的最佳 MRC 模型，其准确率在某些实验中甚至超过了生物医学专家，并释放了三种规模的数据集和代码。

BIOMRC: 一份用于生物医学机器阅读理解的数据集

BIOMRC: A Dataset for Biomedical Machine Reading Comprehension

The COVID-19 Open Research Dataset (CORD-19) is a growing resource of
scientific papers on COVID-19 and related historical coronavirus research.
CORD-19 is designed to facilitate the development of text mining and
information retrieval systems over its rich collection of metadata and
structured full text papers. Since its release, CORD-19 has been downloaded
over 200K times and has served as the basis of many COVID-19 text mining and
discovery systems. In this article, we describe the mechanics of dataset
construction, highlighting challenges and key design decisions, provide an
overview of how CORD-19 has been used, and describe several shared tasks built
around the dataset. We hope this resource will continue to bring together the
computing community, biomedical experts, and policy makers in the search for
effective treatments and management policies for COVID-19.

该研究介绍了 COVID-19 开放研究数据集（CORD-19）的构建、使用和相关挑战，旨在帮助计算机界、医学专家和政策制定者共同探索 COVID-19 的有效治疗和管理政策。