@inproceedings{alvarez-nogales-araque-2024-moral,
    title = "Moral Disagreement over Serious Matters: Discovering the Knowledge Hidden in the Perspectives",
    author = "Alvarez Nogales, Anny D.  and
      Araque, Oscar",
    editor = "Abercrombie, Gavin  and
      Basile, Valerio  and
      Bernadi, Davide  and
      Dudy, Shiran  and
      Frenda, Simona  and
      Havens, Lucy  and
      Tonelli, Sara",
    booktitle = "Proceedings of the 3rd Workshop on Perspectivist Approaches to NLP (NLPerspectives) @ LREC-COLING 2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.nlperspectives-1.7",
    pages = "67--77",
    abstract = "Moral values significantly define decision-making processes, notably on contentious issues like global warming. The Moral Foundations Theory (MFT) delineates morality and aims to reconcile moral expressions across cultures, yet different interpretations arise, posing challenges for computational modeling. This paper addresses the need to incorporate diverse moral perspectives into the learning systems used to estimate morality in text. To do so, it explores how training language models with varied annotator perspectives affects the performance of the learners. Building on top if this, this work also proposes an ensemble method that exploits the diverse perspectives of annotators to construct a more robust moral estimation model. Additionally, we investigate the automated identification of texts that pose annotation challenges, enhancing the understanding of linguistic cues towards annotator disagreement. To evaluate the proposed models we use the Moral Foundations Twitter Corpus (MFTC), a resource that is currently the reference for modeling moral values in computational social sciences. We observe that incorporating the diverse perspectives of annotators into an ensemble model benefits the learning process, showing large improvements in the classification performance. Finally, the results also indicate that instances that convey strong moral meaning are more challenging to annotate.",
}

Moral values significantly define decision-making processes, notably on contentious issues like global warming. The Moral Foundations Theory (MFT) delineates morality and aims to reconcile moral expressions across cultures, yet different interpretations arise, posing challenges for computational modeling. This paper addresses the need to incorporate diverse moral perspectives into the learning systems used to estimate morality in text. To do so, it explores how training language models with varied annotator perspectives affects the performance of the learners. Building on top if this, this work also proposes an ensemble method that exploits the diverse perspectives of annotators to construct a more robust moral estimation model. Additionally, we investigate the automated identification of texts that pose annotation challenges, enhancing the understanding of linguistic cues towards annotator disagreement. To evaluate the proposed models we use the Moral Foundations Twitter Corpus (MFTC), a resource that is currently the reference for modeling moral values in computational social sciences. We observe that incorporating the diverse perspectives of annotators into an ensemble model benefits the learning process, showing large improvements in the classification performance. Finally, the results also indicate that instances that convey strong moral meaning are more challenging to annotate.
Alvarez Nogales, Anny D. Araque, Oscar
Moral Disagreement over Serious Matters: Discovering the Knowledge Hidden in the Perspectives