  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.
