Challenge: Existing studies on character analysis focus on character identification, social network analysis, and the exploration of characters' personas or personalities.
Approach: They propose a four-stage framework to automatically classify actions as moral or immoral based on context.
Outcome: The proposed framework is effective in moral reasoning tasks in multiple genres.

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Text-based inference of moral sentiment change (D19-1)

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Challenge: Existing work in NLP treats moral sentiment as a flat classification problem, but our framework probes moral sentiment change at multiple levels and captures moral dynamics concerning relevance, polarity, and finegrained categories informed by Moral Foundations Theory.
Approach: They propose a text-based framework that exploits implicit moral biases learned from diachronic word embeddings to probe moral sentiment change over a long historical period.
Outcome: The proposed framework supports inferences of historical shifts in moral sentiment toward concepts such as slavery and democracy over centuries at three incremental levels: moral relevance, moral polarity, and fine-grained moral dimensions.
A Corpus for Understanding and Generating Moral Stories (2022.naacl-main)

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Challenge: Existing tasks for evaluating story understanding and generation focus on reasoning plots from context, but they focus on bridging plots with implied morals.
Approach: They propose two understanding tasks and two generation tasks to assess machines' ability to bridge story plots and implied morals.
Outcome: The proposed tasks are based on a dataset of Chinese and English moral stories . they show that the proposed models can perform better than existing models .
CMoralEval: A Moral Evaluation Benchmark for Chinese Large Language Models (2024.findings-acl)

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Challenge: Recent years have witnessed remarkable progress achieved by large language models in both natural language understanding and generation.
Approach: They propose a large benchmark CMoralEval for moral evaluation of Chinese LLMs . they use a Chinese TV program discussing Chinese moral norms and Chinese moral anomies based on various sources .
Outcome: The proposed dataset is characterized by diversity and authenticity.
Story Morals: Surfacing value-driven narrative schemas using large language models (2024.emnlp-main)

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Challenge: Using large language models, we extract and validate story morals across a diverse set of narrative genres.
Approach: They propose a task of narrative schema labelling based on the concept of "story morals" they use large language models to extract and validate story morals across a diverse set of genres .
Outcome: The proposed method extracts and validates story morals across folktales, novels, movies and TV, personal stories from social media and the news using automated metrics and human assessments.
Probing Narrative Morals: A New Character-Focused MFT Framework for Use with Large Language Models (2025.emnlp-main)

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Challenge: Existing methods to categorize moral foundations in storytelling are limited.
Approach: They propose a character-centric method to quantify moral foundations in storytelling using large language models and a novel Moral Foundations Character Action Questionnaire to validate their approach against human annotations.
Outcome: The proposed method validates against human annotations and then applies to 2,697 folktales from 55 countries.
HISTOIRESMORALES: A French Dataset for Assessing Moral Alignment (2025.naacl-long)

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Challenge: HistoiresMorales is a dataset based on moralStories in French . it is based upon annotations of moral values within the dataset .
Approach: They propose a dataset in French that aims to align language models with moral values . they use annotations to ensure their alignment with French norms .
Outcome: The proposed dataset guarantees grammatical accuracy and adaptation to the French cultural context.
Who Plays Which Role? Protagonist Detection and Classification in Moral Discourse (2026.eacl-srw)

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Challenge: a systematic study of phrase-level protagonist detection and classification in moral discourse focuses on moral values rather than the actors involved.
Approach: They propose to decompose a task into identifying protagonist mentions and classifying them by what kind of actor they are and what function they serve in the moral argument.
Outcome: The proposed model outperforms previous models on the Moralization Corpus and fine-tuned lightweight models and prompting-based large language models.
A Survey on Modelling Morality for Text Analysis (2024.findings-acl)

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Challenge: Recent work on modelling morality in text has garnered increasing attention due to its complexity and complexity.
Approach: They provide a systematic review of recent work on modelling morality in text . they discuss challenges and research gaps in the area of NLP .
Outcome: The authors present their work on the modelling of morality in text, which has garnered increasing attention in recent years.
Tales of Morality: Comparing Human- and LLM-Generated Moral Stories from Visual Cues (2025.findings-emnlp)

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Challenge: a recent study has found that stories are central to how humans communicate moral values .
Approach: They compare human- and LLM-generated moral narratives based on images annotated by humans for moral content . authors propose a framework for evaluating moral storytelling in vision-language models .
Outcome: The proposed model compared human- and LLM-generated narratives on images . human stories reflect a balanced distribution of moral foundations and coherent narrative arcs, but LLMs emphasize Care foundation and lack emotional resolution.
Identifying Morality Frames in Political Tweets using Relational Learning (2021.emnlp-main)

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Challenge: Moral sentiment is often motivated by its targets, which can correspond to individuals or collective entities.
Approach: They propose a model to predict moral attitudes towards entities and moral foundations jointly using tweets written by US politicians.
Outcome: The proposed model predicts moral attitudes towards entities and moral foundations jointly from tweets written by US politicians.

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