论文标题
从生物医学文献中检测矛盾的Covid-19药物疗效主张
Detecting Contradictory COVID-19 Drug Efficacy Claims from Biomedical Literature
论文作者
论文摘要
COVID-19大流行创造了有关药物疗效的一系列可疑和矛盾的科学主张，这是一种对科学和社会的持久后果的“流行病”。在这项工作中，我们认为NLP模型可以帮助领域专家提炼并了解这个复杂的高风险领域的文献。我们的任务是自动确定关于19009药物疗效的矛盾主张。我们将其视为自然语言推理问题，并提供由域专家创建的新的NLI数据集。 NLI框架使我们能够创建结合现有数据集和我们自己的课程。最终的模型是有用的调查工具。我们提供了一个案例研究，了解这些模型如何帮助领域专家总结并评估有关Remdisivir和羟基氯喹的证据。
The COVID-19 pandemic created a deluge of questionable and contradictory scientific claims about drug efficacy -- an "infodemic" with lasting consequences for science and society. In this work, we argue that NLP models can help domain experts distill and understand the literature in this complex, high-stakes area. Our task is to automatically identify contradictory claims about COVID-19 drug efficacy. We frame this as a natural language inference problem and offer a new NLI dataset created by domain experts. The NLI framing allows us to create curricula combining existing datasets and our own. The resulting models are useful investigative tools. We provide a case study of how these models help a domain expert summarize and assess evidence concerning remdisivir and hydroxychloroquine.
