TY - DATA
T1 - What does a Text Classifier Learn about Morality? An Explainable Method for Cross-Domain Comparison of Moral Rhetoric - code
PY - 2023/12/18
AU - Enrico Liscio
AU - Oscar Araque
AU - Lorenzo Gatti
AU - Ionut Constantinescu
AU - C.M. (Catholijn) Jonker
AU - Kyriaki Kalimeri
AU - Pradeep K. Murukannaiah
UR - 
DO - 10.4121/1e71138c-be26-4652-971a-48a84837df8e.v1
KW - NLP
KW - Morality
KW - Ethics
KW - XAI
KW - natural language processing
KW - explainable artificial intelligence
N2 - <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>
ER -