@inproceedings{li-etal-2021-winnowing-knowledge,
title = "Winnowing Knowledge for Multi-choice Question Answering",
author = "Li, Yeqiu and
Zou, Bowei and
Li, Zhifeng and
Aw, Ai Ti and
Hong, Yu and
Zhu, Qiaoming",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-emnlp.100/",
doi = "10.18653/v1/2021.findings-emnlp.100",
pages = "1157--1165",
abstract = "We tackle multi-choice question answering. Acquiring related commonsense knowledge to the question and options facilitates the recognition of the correct answer. However, the current reasoning models suffer from the noises in the retrieved knowledge. In this paper, we propose a novel encoding method which is able to conduct interception and soft filtering. This contributes to the harvesting and absorption of representative information with less interference from noises. We experiment on CommonsenseQA. Experimental results illustrate that our method yields substantial and consistent improvements compared to the strong Bert, RoBERTa and Albert-based baselines."
}
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<abstract>We tackle multi-choice question answering. Acquiring related commonsense knowledge to the question and options facilitates the recognition of the correct answer. However, the current reasoning models suffer from the noises in the retrieved knowledge. In this paper, we propose a novel encoding method which is able to conduct interception and soft filtering. This contributes to the harvesting and absorption of representative information with less interference from noises. We experiment on CommonsenseQA. Experimental results illustrate that our method yields substantial and consistent improvements compared to the strong Bert, RoBERTa and Albert-based baselines.</abstract>
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%0 Conference Proceedings
%T Winnowing Knowledge for Multi-choice Question Answering
%A Li, Yeqiu
%A Zou, Bowei
%A Li, Zhifeng
%A Aw, Ai Ti
%A Hong, Yu
%A Zhu, Qiaoming
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F li-etal-2021-winnowing-knowledge
%X We tackle multi-choice question answering. Acquiring related commonsense knowledge to the question and options facilitates the recognition of the correct answer. However, the current reasoning models suffer from the noises in the retrieved knowledge. In this paper, we propose a novel encoding method which is able to conduct interception and soft filtering. This contributes to the harvesting and absorption of representative information with less interference from noises. We experiment on CommonsenseQA. Experimental results illustrate that our method yields substantial and consistent improvements compared to the strong Bert, RoBERTa and Albert-based baselines.
%R 10.18653/v1/2021.findings-emnlp.100
%U https://aclanthology.org/2021.findings-emnlp.100/
%U https://doi.org/10.18653/v1/2021.findings-emnlp.100
%P 1157-1165
Markdown (Informal)
[Winnowing Knowledge for Multi-choice Question Answering](https://aclanthology.org/2021.findings-emnlp.100/) (Li et al., Findings 2021)
ACL
- Yeqiu Li, Bowei Zou, Zhifeng Li, Ai Ti Aw, Yu Hong, and Qiaoming Zhu. 2021. Winnowing Knowledge for Multi-choice Question Answering. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 1157–1165, Punta Cana, Dominican Republic. Association for Computational Linguistics.