Unsupervised Multiple Choices Question Answering: Start Learning from Basic Knowledge

• URL: http://arxiv.org/abs/2010.11003v2
• Date: Mon, 1 Nov 2021 08:40:08 GMT
• Title: Unsupervised Multiple Choices Question Answering: Start Learning from Basic Knowledge
• Authors: Chi-Liang Liu and Hung-yi Lee
• Abstract summary: We study the possibility of almost unsupervised Multiple Choices Question Answering (MCQA) The proposed method is shown to outperform the baseline approaches on RACE and even comparable with some supervised learning approaches on MC500.
• Score: 75.7135212362517
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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