CN-AutoMIC: Distilling Chinese Commonsense Knowledge from Pretrained Language Models

Chenhao Wang, Jiachun Li, Yubo Chen, Kang Liu, Jun Zhao

Abstract
Commonsense knowledge graphs (CKGs) are increasingly applied in various natural language processing tasks. However, most existing CKGs are limited to English, which hinders related research in non-English languages. Meanwhile, directly generating commonsense knowledge from pretrained language models has recently received attention, yet it has not been explored in non-English languages. In this paper, we propose a large-scale Chinese CKG generated from multilingual PLMs, named as **CN-AutoMIC**, aiming to fill the research gap of non-English CKGs. To improve the efficiency, we propose generate-by-category strategy to reduce invalid generation. To ensure the filtering quality, we develop cascaded filters to discard low-quality results. To further increase the diversity and density, we introduce a bootstrapping iteration process to reuse generated results. Finally, we conduct detailed analyses on CN-AutoMIC from different aspects. Empirical results show the proposed CKG has high quality and diversity, surpassing the direct translation version of similar English CKGs. We also find some interesting deficiency patterns and differences between relations, which reveal pending problems in commonsense knowledge generation. We share the resources and related models for further study.
Anthology ID:
2022.emnlp-main.628
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
9253–9265
Language:
URL:
https://aclanthology.org/2022.emnlp-main.628
DOI:
10.18653/v1/2022.emnlp-main.628
Bibkey:
Cite (ACL):
Chenhao Wang, Jiachun Li, Yubo Chen, Kang Liu, and Jun Zhao. 2022. CN-AutoMIC: Distilling Chinese Commonsense Knowledge from Pretrained Language Models. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 9253–9265, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
CN-AutoMIC: Distilling Chinese Commonsense Knowledge from Pretrained Language Models (Wang et al., EMNLP 2022)
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https://preview.aclanthology.org/ingest-acl-2023-videos/2022.emnlp-main.628.pdf
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