Benchmarking progress in Natural Language Processing applications to Moral Foundations Theory.
Moral Foundations Theory (MFT) is a theory of moral psychology that proposes that several univeral foundations underlie human morality. Since its inception, various methods have been proposed to operationalize MFT on natural language data, including dictionary-based approaches and deep neural networks.
NLP applications of MFT have some commonalities with mainstream NLP tasks, but also have some differences. This site aims to cater to the unique needs of MFT NLP approaches, and synchronize with standard ecosystem tools wherever possible.
This site aims to provide an inventory of tasks, datasets, NLP methods for MFT and their performance results, to facilitate the development of improved methods. It is mainly intended as a resource for researchers in NLP, MFT, and related fields.
Uncovering Values: Detecting Latent Moral Content from Natural Language with Explainable and Non-Trained Methods
Moral foundations vignettes: a standardized stimulus database of scenarios based on moral foundations theory