Combination therapies have become the standard of care for diseases such as
cancer, tuberculosis, malaria and HIV. However, the combinatorial set of
available multi-drug treatments creates a challenge in identifying effective
combination therapies available in a situation. To assist medical professionals
in identifying beneficial drug-combinations, we construct an expert-annotated
dataset for extracting information about the efficacy of drug combinations from
the scientific literature. Beyond its practical utility, the dataset also
presents a unique NLP challenge, as the first relation extraction dataset
consisting of variable-length relations. Furthermore, the relations in this
dataset predominantly require language understanding beyond the sentence level,
adding to the challenge of this task. We provide a promising baseline model and
identify clear areas for further improvement. We release our dataset, code, and
baseline models publicly to encourage the NLP community to participate in this
task.

为了帮助医生识别有效的药物联合治疗方案，我们构建了一个专家注释的数据集，用于从科学文献中提取药物联合治疗方案的疗效信息。除了实际应用之外，该数据集还提出了一个独特的 NLP 挑战，作为第一个由可变长度关系组成的关系提取数据集，这种数据集中的关系主要需要超越句子级别的语言理解，增加了此任务的挑战性。我们提供了一个有前途的基线模型，并确定了进一步改进的明显领域。我们公开发布我们的数据集、代码和基线模型，以促进 NLP 社区参与该任务。