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 AbstractTasks
Results from the Paper
Submit
results from this paper
to get state-of-the-art GitHub badges and help the
community compare results to other papers.
Methods
No methods listed for this paper. Add
relevant methods here