Unsupervised Multiple Choices Question Answering: Start Learning from Basic Knowledge
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.
PDF Abstract EMNLP (MRQA) 2021 PDF EMNLP (MRQA) 2021 Abstract
Tasks
SQuAD
RACE
MCTest
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