cff-version: 1.2.0
abstract: "<p>Code for the paper "What does a Text Classifier Learn about Morality? An Explainable Method for Cross-Domain Comparison of Moral Rhetoric", published at ACL '23. This code implements Tomea, an Explainable AI method for investigating the difference in how language models represent morality across domains. Given a pair of datasets and models trained on the datasets, Tomea generates 10 m-distances and one d-distance to measure the difference between the datasets, based on the SHAP method. We make pairwise comparisons of seven models trained on the MFTC datasets (available at this DOI: 10.4121/646b20e3-e24f-452d-938a-bcb6ce30913c<strong>).</strong></p>"
authors:
  - family-names: Liscio
    given-names: Enrico
    orcid: "https://orcid.org/0000-0002-8285-5867"
  - family-names: Araque
    given-names: Oscar
    orcid: "https://orcid.org/0000-0003-3224-0001"
  - family-names: Gatti
    given-names: Lorenzo
    orcid: "https://orcid.org/0000-0003-2422-5055"
  - family-names: Constantinescu
    given-names: Ionut
    orcid: "https://orcid.org/0009-0003-5494-0161"
  - family-names: Jonker
    given-names: C.M. (Catholijn)
    orcid: "https://orcid.org/0000-0003-4780-7461"
  - family-names: Kalimeri
    given-names: Kyriaki
    orcid: "https://orcid.org/0000-0001-8068-5916"
  - family-names: Murukannaiah
    given-names: Pradeep K.
    orcid: "https://orcid.org/0000-0002-1261-6908"
title: "What does a Text Classifier Learn about Morality? An Explainable Method for Cross-Domain Comparison of Moral Rhetoric - code"
keywords:
version: 1
identifiers:
  - type: doi
    value: 10.4121/1e71138c-be26-4652-971a-48a84837df8e.v1
license: MIT
date-released: 2023-12-18