Winnowing Knowledge for Multi-choice Question Answering
Yeqiu Li, Bowei Zou, Zhifeng Li, Ai Ti Aw, Yu Hong, Qiaoming Zhu
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.- Anthology ID:
- 2021.findings-emnlp.100
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2021
- Month:
- November
- Year:
- 2021
- Address:
- Punta Cana, Dominican Republic
- Editors:
- Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
- Venue:
- Findings
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1157–1165
- Language:
- URL:
- https://aclanthology.org/2021.findings-emnlp.100
- DOI:
- 10.18653/v1/2021.findings-emnlp.100
- Cite (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.
- Cite (Informal):
- Winnowing Knowledge for Multi-choice Question Answering (Li et al., Findings 2021)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2021.findings-emnlp.100.pdf
- Data
- CommonsenseQA, ConceptNet