Papers by Catholijn Jonker
What does a Text Classifier Learn about Morality? An Explainable Method for Cross-Domain Comparison of Moral Rhetoric (2023.acl-long)
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Enrico Liscio, Oscar Araque, Lorenzo Gatti, Ionut Constantinescu, Catholijn Jonker, Kyriaki Kalimeri, Pradeep Kumar Murukannaiah
| Challenge: | Existing methods to analyze whether a text classifier learns the domain-specific expression of moral language are lacking. |
| Approach: | They propose a method to compare a supervised classifier’s representation of moral rhetoric across domains by exploring similarities and differences between moral concepts and domains. |
| Outcome: | The proposed method compares a supervised classifier’s representation of moral rhetoric across domains and domains. |
Do Differences in Values Influence Disagreements in Online Discussions? (2023.emnlp-main)
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| Challenge: | Disagreement is an important aspect of online discussions since it can drive novel ideas, incentivize evaluation of the proposed ideas, and avoid echo chambers. |
| Approach: | They propose to use human-annotated agreement labels to estimate personal values and to include value information in agreement prediction to improve performance. |
| Outcome: | The proposed models show that dissimilarity of value profiles correlates with disagreement in specific cases and that including value information in agreement prediction improves performance. |
Cross-Domain Classification of Moral Values (2022.findings-naacl)
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| Challenge: | Existing methods to identify moral values in text can be challenging for transferring knowledge between domains. |
| Approach: | They compare a deep learning model with a domain-specific value classifier to find out whether it can transfer knowledge to new domains. |
| Outcome: | The proposed model can generalize and transfer knowledge to novel domains, but introduce catastrophic forgetting. |
An Empirical Analysis of Diversity in Argument Summarization (2024.eacl-long)
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| Challenge: | Current methods for summarizing arguments miss an important aspect of diversity . authors examine three aspects of diversity in argument summarization . |
| Approach: | They propose three aspects of diversity that are important for accommodating multiple perspectives. |
| Outcome: | The proposed models lack the diversity of opinions, sources, and annotators. |