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ME2-BERT: Are Events and Emotions what you need for Moral Foundation Prediction?

Paper · 2025 · Lorenzo Zangari, Candida M. Greco, Davide Picca et al.

Summary

Moral Foundation Prediction tries to detect which Moral Foundations Theory (MFT) dimensions a text expresses. Earlier systems usually look at morality alone and transfer poorly to new domains. The authors propose ME2-BERT, a BERT fine-tuning framework that also uses emotions and events. It is trained on E2MoCase_full, a news corpus that the authors describe as the only dataset with joint labels for morality, emotions and events.

The framework works as follows. Paragraphs with events and paragraphs without them are treated as separate domains. A denoising auto-encoder, guided by contrastive learning over emotion categories, produces text embeddings. An adversarial event-based domain classifier pushes these embeddings to be domain-invariant, and a multi-label moral classifier predicts the foundations.

ME2-BERT beats lexicon-based, BERT-based and LLM baselines. Its average F1 gains range from about 15% to 33%, and the gain reaches 35% when testing out of domain. Ablations indicate that every component, including the emotion and event modules, helps. This suggests that emotions and events give useful signals for robust morality detection.

Key points

  • The task is framed as multi-label classification over the five MFT foundations: Care/Harm, Fairness/Cheating, Loyalty/Betrayal, Authority/Subversion and Purity/Degradation. Texts with none of them count as non-moral.
  • The training data is E2MoCase_full (Greco et al., 2024). Emotion categories follow Plutchik's wheel, and each text is assigned a single dominant emotion.
  • The architecture combines unsupervised domain adaptation, an emotion-aware denoising auto-encoder, a contrastive loss, an adversarial domain classifier and a moral classifier.
  • The event-based domain strategy also worked on the MoralEvent dataset, where ME2-BERT outperformed BERT-base. Sentence-Transformer encoders were tested as well.
  • Code and trained models are available at mlnteam-unical.github.io/resources/.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Fields, topics and concepts

📚 Shelves 2The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 works

🧭 Topics 1Research subjects and languages the work is about.Sentiment & emotion · 972 works

Cite

Cite as Lorenzo Zangari, Candida M. Greco, Davide Picca and Andrea Tagarelli. 2025. ME2-BERT: Are Events and Emotions what you need for Moral Foundation Prediction?. Proceedings of the 31st International Conference on Computational Linguistics.

The paper itself, not this page. BibTeX from the library's record of the work.

@inproceedings{zangari2025me2,
  title = {{ME2-BERT: Are Events and Emotions what you need for Moral Foundation Prediction?}},
  author = {Lorenzo Zangari, Candida M. Greco, Davide Picca and Andrea Tagarelli},
  booktitle = {Proceedings of the 31st International Conference on Computational Linguistics},
  year = {2025},
  url = {https://aclanthology.org/2025.coling-main.638/}
}

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