# A Data Fusion Framework for Multi-Domain Morality Learning

arXiv: https://arxiv.org/abs/2304.02144 · HTML: https://mlsanity.com/paper/2304.02144/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 47 references that are in the map.
- First posted on arXiv 4 Apr 2023.

## Builds on (most cited first, 21 of 21)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [Social Chemistry 101: Learning to Reason about Social and Moral Norms](https://mlsanity.com/paper/2011.00620/) (2020)
- [Large Pre-trained Language Models Contain Human-like Biases of What is Right and Wrong to Do](https://mlsanity.com/paper/2103.11790/) (2021)
- [Adversarial Filters of Dataset Biases](https://mlsanity.com/paper/2002.04108/) (2020)
- [Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift](https://mlsanity.com/paper/2003.04475/) (2020)
- [Multi-Source Domain Adaptation with Mixture of Experts](https://mlsanity.com/paper/1809.02256/) (2018)
- [Adversarial Learning for Zero-Shot Stance Detection on Social Media](https://mlsanity.com/paper/2105.06603/) (2021)
- [Cross-Domain Label-Adaptive Stance Detection](https://mlsanity.com/paper/2104.07467/) (2021)
- [Moral Framing and Ideological Bias of News](https://mlsanity.com/paper/2009.12979/) (2020)
- [Acquiring Background Knowledge to Improve Moral Value Prediction](https://mlsanity.com/paper/1709.05467/) (2017)
- [The Moral Foundations Reddit Corpus](https://mlsanity.com/paper/2208.05545/) (2022)
- [A Holistic Framework for Analyzing the COVID-19 Vaccine Debate](https://mlsanity.com/paper/2205.01817/) (2022)
- [Learning Semantic Segmentation from Multiple Datasets with Label Shifts](https://mlsanity.com/paper/2202.14030/) (2022)
- [Crowdsourcing Learning as Domain Adaptation: A Case Study on Named Entity Recognition](https://mlsanity.com/paper/2105.14980/) (2021)
- [Aspect-augmented Adversarial Networks for Domain Adaptation](https://mlsanity.com/paper/1701.00188/) (2017)
- [Modeling Ideological Salience and Framing in Polarized Online Groups with Graph Neural Networks and Structured Sparsity](https://mlsanity.com/paper/2104.08829/) (2021)
- [Understanding COVID-19 Vaccine Campaign on Facebook using Minimal Supervision](https://mlsanity.com/paper/2210.10031/) (2022)
- [Contextualized moral inference](https://mlsanity.com/paper/2008.10762/) (2020)

## Built on by (most cited first, 14 of 14)

- [Emergence of Social Norms in Generative Agent Societies: Principles and Architecture](https://mlsanity.com/paper/2403.08251/) (2024)
- [MoralBERT: A Fine-Tuned Language Model for Capturing Moral Values in Social Discussions](https://mlsanity.com/paper/2403.07678/) (2024)
- [Whose Emotions and Moral Sentiments Do Language Models Reflect?](https://mlsanity.com/paper/2402.11114/) (2024)
- [MoVa: Towards Generalizable Classification of Human Morals and Values](https://mlsanity.com/paper/2509.24216/) (2025)
- [Inducing Political Bias Allows Language Models Anticipate Partisan Reactions to Controversies](https://mlsanity.com/paper/2311.09687/) (2023)
- [Estimating Causal Effects of Text Interventions Leveraging LLMs](https://mlsanity.com/paper/2410.21474/) (2024)
- [A Survey on Moral Foundation Theory and Pre-Trained Language Models: Current Advances and Challenges](https://mlsanity.com/paper/2409.13521/) (2024)
- [Fairness Metric Design Exploration in Multi-Domain Moral Sentiment Classification using Transformer-Based Models](https://mlsanity.com/paper/2510.11222/) (2025)
- [#EpiTwitter: Public Health Messaging During the COVID-19 Pandemic](https://mlsanity.com/paper/2406.01866/) (2024)
- [Do Language Models Understand Morality? Towards a Robust Detection of Moral Content](https://mlsanity.com/paper/2406.04143/) (2024)
- [MoralCLIP: Contrastive Alignment of Vision-and-Language Representations with Moral Foundations Theory](https://mlsanity.com/paper/2506.05696/) (2025)
- [TWeddit : A Dataset of Triggering Stories Predominantly Shared by Women on Reddit](https://mlsanity.com/paper/2601.11819/) (2026)
- [Learning Moral Diversity: Modelling Individual Perspectives in Moral Classification of Texts](https://mlsanity.com/paper/2606.22771/) (2026)
- [Less Is Moral: A CHARMing Framework for Moral Foundations Detection in Endorsement Behaviour](https://mlsanity.com/paper/2609.03330/) (2026)

## Abstract (arXiv)

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.