This paper introduces FrenchMedMCQA, the first publicly available
Multiple-Choice Question Answering (MCQA) dataset in French for medical domain.
It is composed of 3,105 questions taken from real exams of the French medical
specialization diploma in pharmacy, mixing single and multiple answers. Each
instance of the dataset contains an identifier, a question, five possible
answers and their manual correction(s). We also propose first baseline models
to automatically process this MCQA task in order to report on the current
performances and to highlight the difficulty of the task. A detailed analysis
of the results showed that it is necessary to have representations adapted to
the medical domain or to the MCQA task: in our case, English specialized models
yielded better results than generic French ones, even though FrenchMedMCQA is
in French. Corpus, models and tools are available online.

该论文介绍了 FrenchMedMCQA，即用于医学领域的多项选择问答（MCQA）的首个公开数据集，其中包含从法国药学专业毕业考试中提取的 3105 个问题，使用单选和多选题混合，提供基线模型以自动处理此 MCQA 任务。

FrenchMedMCQA：一份针对医学领域的法语多项选择题答题数据集

FrenchMedMCQA: A French Multiple-Choice Question Answering Dataset for  Medical domain

In this paper, we study the possibility of almost unsupervised Multiple
Choices Question Answering (MCQA). Starting from very basic knowledge, MCQA
model knows that some choices have higher probabilities of being correct than
the others. The information, though very noisy, guides the training of an MCQA
model. The proposed method is shown to outperform the baseline approaches on
RACE and even comparable with some supervised learning approaches on MC500.

本文研究了几乎无监督的多项选择题回答（MCQA）的可能性，从非常基本的知识开始，MCQA 模型知道某些选择的正确概率比其他选择更高，提出的方法在 RACE 上胜过基准方法，并且与一些监督学习方法在 MC500 上相当。