4 research outputs found
A Data Fusion Framework for Multi-Domain Morality Learning
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
Non-Binary Gender Expression in Online Interactions
Many openly non-binary gender individuals participate in social networks.
However, the relationship between gender and online interactions is not well
understood, which may result in disparate treatment by large language models.
We investigate individual identity on Twitter, focusing on gender expression as
represented by users chosen pronouns. We find that non-binary groups tend to
receive less attention in the form of likes and followers. We also find that
nonbinary users send and receive tweets with above-average toxicity. The study
highlights the importance of considering gender as a spectrum, rather than a
binary, in understanding online interactions and expression
A Data Fusion Framework for Multi-Domain Morality Learning
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