Papers by Béla Neuendorf
Mitigating Toxic Degeneration with Empathetic Data: Exploring the Relationship Between Toxicity and Empathy (2022.naacl-main)
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| Challenge: | Recent work on controllable text generation has shown promise in successfully altering such text attributes. |
| Approach: | They propose to use empathetic data to reduce the toxicity of generated text by strategically sampling data based on empathy scores. |
| Outcome: | The proposed model significantly reduces the size of fine-tuning data to 7.5-30k samples while making significant improvements over state-of-the-art toxicity mitigation. |
Appraisal Framework for Clinical Empathy: A Novel Application to Breaking Bad News Conversations (2024.lrec-main)
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| Challenge: | Empathy is essential in healthcare communication. |
| Approach: | They propose an annotation approach that draws on well-established frameworks for clinical empathy and breaking bad news conversations for considering the dynamic dynamics of discourse relations. |
| Outcome: | The proposed model can be used to train models to detect causal relations involving empathy, a feature of systems that can provide feedback to medical professionals in training. |
Corpus Considerations for Annotator Modeling and Scaling (2024.naacl-long)
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| Challenge: | Recent trends in natural language processing and annotation tasks emphasize individual perspectives . annotator models that rely on a single ground truth may disregard valuable minority perspectives omissions . |
| Approach: | They propose a composite embedding approach to investigate annotator modeling techniques . they show that the commonly used user token model consistently outperforms more complex models . |
| Outcome: | The proposed model outperforms more complex models on a given dataset. |
Unifying Data Perspectivism and Personalization: An Application to Social Norms (2022.emnlp-main)
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| Challenge: | Obtaining a single ground truth is not possible or necessary for subjective tasks. |
| Approach: | They propose a set of personalization methods to model annotators and compare their effectiveness for predicting social norms. |
| Outcome: | The proposed model outperforms existing models and compares performance across subsets of social situations that vary by the closeness of the relationship between parties in conflict. |