cff-version: 1.2.0
abstract: "<p>We train embedding spaces with the MFTC corpus, to see how an embedding space can learn the distribution of pluralist morality. We compare off-the-shelf, unsupervised, and supervised approaches, showing that a supervised approach is necessary. Here, you can find the models we trained with unsupervised and supervised approaches.</p>"
authors:
  - family-names: Park
    given-names: Jeongwoo
    orcid: "https://orcid.org/0009-0006-9686-7603"
  - family-names: Liscio
    given-names: Enrico
    orcid: "https://orcid.org/0000-0002-8285-5867"
  - family-names: Murukannaiah
    given-names: Pradeep K.
    orcid: "https://orcid.org/0000-0002-1261-6908"
title: "Morality is Non-Binary: Building a Pluralist Moral Sentence Embedding Space using Contrastive Learning - models"
keywords:
version: 1
identifiers:
  - type: doi
    value: 10.4121/e0d75aad-6cd1-45dd-a5ec-985e399337b4.v1
license: CC BY 4.0
date-released: 2024-01-30