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
- Editors:
- Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 1250–1262
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.107
- DOI:
- 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)
- PDF:
- https://preview.aclanthology.org/corrections-2024-05/2022.coling-1.107.pdf