A Data Fusion Framework for Multi-Domain Morality Learning
This addresses the challenge of poor generalization in morality learning for researchers and practitioners by providing a method to effectively combine diverse datasets.
The paper tackles the problem of training language models on multiple heterogeneous morality datasets by proposing a data fusion framework that improves performance and generalizability, achieving state-of-the-art results in morality inference.
Language models can be trained to recognize the moral sentiment of text, creating new opportunities to study the role of morality in human life. As interest in language and morality has grown, several ground truth datasets with moral annotations have been released. However, these datasets vary in the method of data collection, domain, topics, instructions for annotators, etc. Simply aggregating such heterogeneous datasets during training can yield models that fail to generalize well. We describe a data fusion framework for training on multiple heterogeneous datasets that improve performance and generalizability. The model uses domain adversarial training to align the datasets in feature space and a weighted loss function to deal with label shift. We show that the proposed framework achieves state-of-the-art performance in different datasets compared to prior works in morality inference.