Year: 2026
Type: conference-paper
Abstract: Protagonists play a central role in moral discourse by structuring responsibility and authority, yet computational work has largely focused on moral values rather than the actors involved.We address this gap by studying phrase-level protagonist detection and classification in the Moralization Corpus (Becker et al., 2025), a dataset of moral arguments across different text genres.We decompose the task into identifying protagonist mentions and classifying them by what kind of actor they are (e.g., individual or institution) and what function they serve in the moral argument.We compare finetuned lightweight models, state-of-the-art NER models, and prompting-based large language models.We further establish human baselines and analyze the impact of contextual information on human and model decisions.Our results show that fine-tuned NER models achieve competitive detection performance at substantially lower cost than prompted large language models, and that role classification benefits more strongly from contextualized prompting.Across tasks, top-performing models reach or exceed human-level performance, highlighting the value of task decomposition for modeling protagonists in moral discourse.We release our code, predictions, and supplementary material in our project repository. 1 Neele Falk and Gabriella Lapesa.2025.Mining the uncertainty patterns of humans and models in the annotation of moral foundations and human values.In
Authors: Mirko Sommer, Maria Becker
Institution: Heidelberg University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7140103084