Research output per year
Research output per year
Description
Raw results and crowd task material 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 repository contains two main folders. First, the instructions and informed consent used to perform the crowd study described in the paper, where crowd workers are asked to compare word bubbles describing moral values in a domain. Second, the results of the pairwise comparisons performed with the seven models trained on the MFTC datasets and the comparison of the results with the crowd annotations.
The code to generate the results is available at this DOI: 10.4121/1e71138c-be26-4652-971a-48a84837df8e
The seven models are available at this DOI: 10.4121/646b20e3-e24f-452d-938a-bcb6ce30913c
The code to generate the results is available at this DOI: 10.4121/1e71138c-be26-4652-971a-48a84837df8e
The seven models are available at this DOI: 10.4121/646b20e3-e24f-452d-938a-bcb6ce30913c
Bibliographical note
creator: Ionut Constantinescu
creator: Kyriaki Kalimeri
creator: Kyriaki Kalimeri
| Date made available | 18 Dec 2023 |
|---|---|
| Publisher | TU Delft - 4TU.ResearchData |
| Date of data production | 2023 - |
Research output
- 1 Conference contribution
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What does a Text Classifier Learn about Morality? An Explainable Method for Cross-Domain Comparison of Moral Rhetoric
Liscio, E., Araque, O., Gatti, L., Constantinescu, I. L., Jonker, C. M., Kalimeri, K. & Murukannaiah, P. K., 2023, Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics Volume 1: Long Papers. Association for Computational Linguistics (ACL), p. 14113–14132 20 p. (ACL '23).Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review
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