Unsupervised Deep Learning based Multiple Choices Question Answering: Start Learning from Basic Knowledge.
Year: 2020
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Chi-Liang Liu, Hung-yi Lee
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3094331324