Papers by Philipp Heinisch
From Argumentation to Deliberation: Perspectivized Stance Vectors for Fine-grained (Dis)agreement Analysis (2025.findings-naacl)
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| Challenge: | Existing methods to identify conflict resolution points require a deeper analysis of arguments and the perspectives they are grounded in. |
| Approach: | They propose a framework for a deliberative analysis of arguments in a computational argumentation setup. |
| Outcome: | The proposed framework allows us to identify actionable options for conflict resolution, as a first step towards deliberation. |
Architectural Sweet Spots for Modeling Human Label Variation by the Example of Argument Quality: It’s Best to Relate Perspectives! (2023.emnlp-main)
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| Challenge: | Existing approaches to subjectivity in natural language processing are subjective . authors argue that disagreement should not be regarded as a problem . |
| Approach: | They propose to account for subjective perspectives of individuals and objective concepts that build a common ground between annotators. |
| Outcome: | The proposed architectures increase the averaged annotator-individual F1-scores up to 43% over a majority-label model. |
“Tell me who you are and I tell you how you argue”: Predicting Stances and Arguments for Stakeholder Groups (2024.findings-naacl)
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| Challenge: | Argument mining has focused on the identification, extraction, and formalization of arguments. |
| Approach: | They propose a framework that relies on a recommender-based architecture to predict stances and argumentative main points on societally controversial topics for a given stakeholder. |
| Outcome: | The proposed framework predicts arguments on a debate topic based on BERTScore and debate.org datasets. |
Similarity-weighted Construction of Contextualized Commonsense Knowledge Graphs for Knowledge-intense Argumentation Tasks (2023.acl-long)
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| Challenge: | Arguments often do not make explicit how a conclusion follows from its premises . we present a method for constructing Contextualized Commonsense Knowledge Graphs (CCKGs) that is efficient and high-quality . |
| Approach: | They propose an unsupervised method for constructing Contextualized Commonsense Knowledge Graphs (CCKGs) they use triplet similarities to extract contextually relevant knowledge paths . |
| Outcome: | The proposed method outperforms baselines and a GPT-3 based system in a knowledge-intense argumentation task. |