Abstract
Large-scale language models have been reducing the gap between machines and humans in understanding the real world, yet understanding an individual’s theory of mind and behavior from text is far from being resolved. This research proposes a neural model—Subjective Ground Attention—that learns subjective grounds of individuals and accounts for their judgments on situations of others posted on social media. Using simple attention modules as well as taking one’s previous activities into consideration, we empirically show that our model provides human-readable explanations of an individual’s subjective preference in judging social situations. We further qualitatively evaluate the explanations generated by the model and claim that our model learns an individual’s subjective orientation towards abstract moral concepts.- Anthology ID:
- 2022.findings-emnlp.126
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2022
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1752–1766
- Language:
- URL:
- https://aclanthology.org/2022.findings-emnlp.126
- DOI:
- Cite (ACL):
- Younghun Lee and Dan Goldwasser. 2022. Towards Explaining Subjective Ground of Individuals on Social Media. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 1752–1766, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- Towards Explaining Subjective Ground of Individuals on Social Media (Lee & Goldwasser, Findings 2022)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.findings-emnlp.126.pdf