| Boundary detection and categorization of argument aspects via supervised learning M Ruckdeschel, G Wiedemann Proceedings of the 9th Workshop on Argument Mining, 126-136, 2022 | 14 | 2022 |
| Few-shot learning for argument aspects of the nuclear energy debate L Jurkschat, G Wiedemann, M Heinrich, M Ruckdeschel, S Torge Proceedings of the Thirteenth Language Resources and Evaluation Conference …, 2022 | 7 | 2022 |
| Argument Mining of Attack and Support Patterns in Dialogical Conversations with Sequential Pattern Mining M Ruckdeschel, R Baumann, G Wiedemann Conference on Advances in Robust Argumentation Machines, 39-56, 2024 | 1 | 2024 |
| Few-shot learning for automated content analysis: efficient coding of arguments and claims in the debate on arms deliveries to Ukraine J Rieger, K Yanchenko, M Ruckdeschel, G von Nordheim, KK Königslöw, ... arXiv preprint arXiv:2312.16975, 2023 | 1 | 2023 |
| Just Read the Codebook! Make Use of Quality Codebooks in Zero-Shot Classification of Multilabel Frame Datasets M Ruckdeschel Proceedings of the 31st International Conference on Computational …, 2025 | | 2025 |
| PETapter: Leveraging PET-style classification heads for modular few-shot parameter-efficient fine-tuning J Rieger, M Ruckdeschel, G Wiedemann arXiv preprint arXiv:2412.04975, 2024 | | 2024 |
| Term-based and Embedding-based Similarity Search in Large Unknown Text Datasets M Ruckdeschel | | 2020 |
| Few-shot learning for automated content analysis (FLACA) J Rieger, M Ruckdeschel, K Yanchenko, G von Nordheim, ... | | |