首个多语言医学问答解释数据集，助力医生训练推理能力。
CasiMedicos-Arg: A Medical Question Answering Dataset Annotated with Explanatory Argumentative Structures
- 构建多语言医学问答数据集，含医生撰写的真实解释
- 标注5021个主张、2313个前提及数千条支持/攻击关系
- 适合医疗AI可解释性、临床推理训练研究者使用
当前人工智能在敏感领域如医疗中的决策解释仍是重大挑战。同样，人类诊疗也需阐明判断依据。住院医师不仅需给出正确诊断，还需说明推理过程。为此，本文首次提出多语言医学问答数据集CasiMedicos-Arg，包含英语、西班牙语、法语、意大利语共558个临床案例，每个案例均配有医生撰写的自然语言解释，并人工标注了论证组件（主张、前提）与论证关系（支持、攻击）。全数据集共标注5021个主张、2313个前提、2431个支持关系和1106个攻击关系。实验表明，现有基线模型在此复杂论证挖掘任务上表现有限，凸显其挑战性。
原文摘要 · Abstract (English)
Explaining Artificial Intelligence (AI) decisions is a major challenge nowadays in AI, in particular when applied to sensitive scenarios like medicine and law. However, the need to explain the rationale behind decisions is a main issue also for human-based deliberation as it is important to justify \textit{why} a certain decision has been taken. Resident medical doctors for instance are required not only to provide a (possibly correct) diagnosis, but also to explain how they reached a certain conclusion. Developing new tools to aid residents to train their explanation skills is therefore a central objective of AI in education. In this paper, we follow this direction, and we present, to the best of our knowledge, the first multilingual dataset for Medical Question Answering where correct and incorrect diagnoses for a clinical case are enriched with a natural language explanation written by doctors. These explanations have been manually annotated with argument components (i.e., premise, claim) and argument relations (i.e., attack, support), resulting in the Multilingual CasiMedicos-Arg dataset which consists of 558 clinical cases in four languages (English, Spanish, French, Italian) with explanations, where we annotated 5021 claims, 2313 premises, 2431 support relations, and 1106 attack relations. We conclude by showing how competitive baselines perform over this challenging dataset for the argument mining task.
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