From Polarity to Intensity: Mining Morality from Semantic Space

Chunxu Zhao, Pengyuan Liu, Dong Yu


Abstract
Most works on computational morality focus on moral polarity recognition, i.e., distinguishing right from wrong. However, a discrete polarity label is not informative enough to reflect morality as it does not contain any degree or intensity information. Existing approaches to compute moral intensity are limited to word-level measurement and heavily rely on human labelling. In this paper, we propose MoralScore, a weakly-supervised framework that can automatically measure moral intensity from text. It only needs moral polarity labels, which are more robust and easier to acquire. Besides, the framework can capture latent moral information not only from words but also from sentence-level semantics which can provide a more comprehensive measurement. To evaluate the performance of our method, we introduce a set of evaluation metrics and conduct extensive experiments. Results show that our method achieves good performance on both automatic and human evaluations.
Anthology ID:
2022.coling-1.107
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
1250–1262
Language:
URL:
https://aclanthology.org/2022.coling-1.107
DOI:
Bibkey:
Cite (ACL):
Chunxu Zhao, Pengyuan Liu, and Dong Yu. 2022. From Polarity to Intensity: Mining Morality from Semantic Space. In Proceedings of the 29th International Conference on Computational Linguistics, pages 1250–1262, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
From Polarity to Intensity: Mining Morality from Semantic Space (Zhao et al., COLING 2022)
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PDF:
https://aclanthology.org/2022.coling-1.107.pdf