TSGP: Two-Stage Generative Prompting for Unsupervised Commonsense Question Answering

Yueqing Sun, Yu Zhang, Le Qi, Qi Shi


Abstract
Without training on labeled task data, unsupervised commonsense question answering seems challenging since it requires commonsense knowledge beyond the context of questions. Previous methods typically retrieved from traditional knowledge bases or used pre-trained language models (PrLMs) to generate fixed types of knowledge, which have poor generalization ability.In this paper, we aim to address the above limitation by leveraging the implicit knowledge stored in PrLMs and propose a two-stage prompt-based unsupervised commonsense question answering framework (TSGP). We first use knowledge generation prompts to generate the knowledge required for questions with unlimited types and possible candidate answers independent of specified choices. Then, we further utilize answer generation prompts to generate possible candidate answers independent of specified choices. Experimental results and analysis on three different commonsense reasoning tasks, CommonsenseQA, OpenBookQA, and SocialIQA, demonstrate that TSGP significantly improves the reasoning ability of language models in unsupervised settings.
Anthology ID:
2022.findings-emnlp.68
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2022
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
968–980
Language:
URL:
https://aclanthology.org/2022.findings-emnlp.68
DOI:
10.18653/v1/2022.findings-emnlp.68
Bibkey:
Cite (ACL):
Yueqing Sun, Yu Zhang, Le Qi, and Qi Shi. 2022. TSGP: Two-Stage Generative Prompting for Unsupervised Commonsense Question Answering. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 968–980, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
TSGP: Two-Stage Generative Prompting for Unsupervised Commonsense Question Answering (Sun et al., Findings 2022)
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PDF:
https://aclanthology.org/2022.findings-emnlp.68.pdf