@inproceedings{liu-lee-2021-unsupervised,
title = "Unsupervised Multiple Choices Question Answering: Start Learning from Basic Knowledge",
author = "Liu, Chi-Liang and
Lee, Hung-yi",
editor = "Fisch, Adam and
Talmor, Alon and
Chen, Danqi and
Choi, Eunsol and
Seo, Minjoon and
Lewis, Patrick and
Jia, Robin and
Min, Sewon",
booktitle = "Proceedings of the 3rd Workshop on Machine Reading for Question Answering",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.mrqa-1.12",
doi = "10.18653/v1/2021.mrqa-1.12",
pages = "113--118",
abstract = "In this paper, we study the possibility of unsupervised Multiple Choices Question Answering (MCQA). From very basic knowledge, the MCQA model knows that some choices have higher probabilities of being correct than 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 is even comparable with some supervised learning approaches on MC500.",
}
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<abstract>In this paper, we study the possibility of unsupervised Multiple Choices Question Answering (MCQA). From very basic knowledge, the MCQA model knows that some choices have higher probabilities of being correct than 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 is even comparable with some supervised learning approaches on MC500.</abstract>
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%0 Conference Proceedings
%T Unsupervised Multiple Choices Question Answering: Start Learning from Basic Knowledge
%A Liu, Chi-Liang
%A Lee, Hung-yi
%Y Fisch, Adam
%Y Talmor, Alon
%Y Chen, Danqi
%Y Choi, Eunsol
%Y Seo, Minjoon
%Y Lewis, Patrick
%Y Jia, Robin
%Y Min, Sewon
%S Proceedings of the 3rd Workshop on Machine Reading for Question Answering
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F liu-lee-2021-unsupervised
%X In this paper, we study the possibility of unsupervised Multiple Choices Question Answering (MCQA). From very basic knowledge, the MCQA model knows that some choices have higher probabilities of being correct than 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 is even comparable with some supervised learning approaches on MC500.
%R 10.18653/v1/2021.mrqa-1.12
%U https://aclanthology.org/2021.mrqa-1.12
%U https://doi.org/10.18653/v1/2021.mrqa-1.12
%P 113-118
Markdown (Informal)
[Unsupervised Multiple Choices Question Answering: Start Learning from Basic Knowledge](https://aclanthology.org/2021.mrqa-1.12) (Liu & Lee, MRQA 2021)
ACL