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Unsupervised Multiple Choices Question Answering: Start Learning from Basic Knowledge
October 21, 2020 ยท Declared Dead ยท ๐ Workshop on Machine Reading for Question Answering
"No code URL or promise found in abstract"
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Authors
Chi-Liang Liu, Hung-yi Lee
arXiv ID
2010.11003
Category
cs.CL: Computation & Language
Cross-listed
cs.AI
Citations
4
Venue
Workshop on Machine Reading for Question Answering
Last Checked
4 months ago
Abstract
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.
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