论文标题
无监督的多种选择问题回答：开始从基本知识中学习
Unsupervised Multiple Choices Question Answering: Start Learning from Basic Knowledge
论文作者
论文摘要
在本文中，我们研究了几乎无监督的多个选择答案的可能性（MCQA）。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。这些信息虽然非常嘈杂，但却指导了MCQA模型的培训。所提出的方法显示出优于种族的基线方法，甚至与MC500上的一些监督学习方法相媲美。
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.
