Evidence Inference 2.0: More Data, Better Models
Evidence Inference 2.0: More Data, Better Models
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DOI：
10.18653/v1/2020.bionlp-1.13
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发表时间：
2020-05
期刊：
影响因子：
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通讯作者：
Jay DeYoung;Eric P. Lehman;Benjamin E. Nye;I. Marshall;Byron C. Wallace
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文献类型：
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作者：
Jay DeYoung;Eric P. Lehman;Benjamin E. Nye;I. Marshall;Byron C. Wallace
我们如何最有效地治疗一种疾病或状况？理想情况下，我们可以参考从临床试验中收集的证据数据库来回答这些问题。不幸的是，没有这样的数据库存在；相反，临床试验结果主要通过冗长的自然语言文章传播。对于医疗保健从业者来说，阅读所有这些文章将非常耗时；相反，他们倾向于依靠人工编制的医学文献系统综述来为护理提供信息。NLP可能会加快这一过程，并最终促进对已发表证据的即时查阅。证据推理数据集最近发布，以促进这方面的研究。这项任务需要从特定文章（描述临床试验）中推断两种治疗方法相对于给定结果的比较性能，并确定支持证据。例如：这篇文章是否报道了化疗在可手术癌症的五年生存率方面优于手术？在本文中，我们收集了额外的注释，将证据推理数据集扩展了25%，提供了更强的基线模型，系统地检查了这些错误，并探测了数据集的质量。我们还发布了一个任务的抽象版本（与全文相反），用于快速模型原型。新基线和评估的更新语料库、文档和代码可在http://evidence-inference.ebm-nlp.com/上获得。
How do we most effectively treat a disease or condition? Ideally, we could consult a database of evidence gleaned from clinical trials to answer such questions. Unfortunately, no such database exists; clinical trial results are instead disseminated primarily via lengthy natural language articles. Perusing all such articles would be prohibitively time-consuming for healthcare practitioners; they instead tend to depend on manually compiled systematic reviews of medical literature to inform care. NLP may speed this process up, and eventually facilitate immediate consult of published evidence. The Evidence Inference dataset was recently released to facilitate research toward this end. This task entails inferring the comparative performance of two treatments, with respect to a given outcome, from a particular article (describing a clinical trial) and identifying supporting evidence. For instance: Does this article report that chemotherapy performed better than surgery for five-year survival rates of operable cancers? In this paper, we collect additional annotations to expand the Evidence Inference dataset by 25%, provide stronger baseline models, systematically inspect the errors that these make, and probe dataset quality. We also release an abstract only (as opposed to full-texts) version of the task for rapid model prototyping. The updated corpus, documentation, and code for new baselines and evaluations are available at http://evidence-inference.ebm-nlp.com/.