@inproceedings{sun-etal-2022-tsgp,
title = "{TSGP}: Two-Stage Generative Prompting for Unsupervised Commonsense Question Answering",
author = "Sun, Yueqing and
Zhang, Yu and
Qi, Le and
Shi, Qi",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.68",
doi = "10.18653/v1/2022.findings-emnlp.68",
pages = "968--980",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T TSGP: Two-Stage Generative Prompting for Unsupervised Commonsense Question Answering
%A Sun, Yueqing
%A Zhang, Yu
%A Qi, Le
%A Shi, Qi
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F sun-etal-2022-tsgp
%X 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.
%R 10.18653/v1/2022.findings-emnlp.68
%U https://aclanthology.org/2022.findings-emnlp.68
%U https://doi.org/10.18653/v1/2022.findings-emnlp.68
%P 968-980
Markdown (Informal)
[TSGP: Two-Stage Generative Prompting for Unsupervised Commonsense Question Answering](https://aclanthology.org/2022.findings-emnlp.68) (Sun et al., Findings 2022)
ACL