How can we interpret and retrieve medical evidence to support clinical
decisions? Clinical trial reports (CTR) amassed over the years contain
indispensable information for the development of personalized medicine.
However, it is practically infeasible to manually inspect over 400,000+
clinical trial reports in order to find the best evidence for experimental
treatments. Natural Language Inference (NLI) offers a potential solution to
this problem, by allowing the scalable computation of textual entailment.
However, existing NLI models perform poorly on biomedical corpora, and
previously published datasets fail to capture the full complexity of inference
over CTRs. In this work, we present a novel resource to advance research on NLI
for reasoning on CTRs. The resource includes two main tasks. Firstly, to
determine the inference relation between a natural language statement, and a
CTR. Secondly, to retrieve supporting facts to justify the predicted relation.
We provide NLI4CT, a corpus of 2400 statements and CTRs, annotated for these
tasks. Baselines on this corpus expose the limitations of existing NLI models,
with 6 state-of-the-art NLI models achieving a maximum F1 score of 0.627. To
the best of our knowledge, we are the first to design a task that covers the
interpretation of full CTRs. To encourage further work on this challenging
dataset, we make the corpus, competition leaderboard, website and code to
replicate the baseline experiments available at:
this https URL

本篇论文介绍了一个新的 NLI4CT 资源，包括两个主要任务：确定自然语言陈述和 CTR 之间的推理关系以及检索支持事实以证明预测关系。该资源暴露了现有 NLI 模型的局限性，为 CTR 推理提供了基础，大大推进了 CTR 的个性化医疗领域的研究。