Boundary Detection and Categorization of Argument Aspects via Supervised Learning. - Mattes Ruckdeschel; Gregor Wiedemann (ArgMining@COLING, 2022)

Few-Shot Learning for Argument Aspects of the Nuclear Energy Debate. - Lena Jurkschat; Gregor Wiedemann; Maximilian Heinrich; Mattes Ruckdeschel; Sunna Torge 001 (LREC, 2022)

Few-shot learning for automated content analysis: Efficient coding of arguments and claims in the debate on arms deliveries to Ukraine. - Jonas Rieger; Kostiantyn Yanchenko; Mattes Ruckdeschel; Gerret von Nordheim; Katharina Kleinen-von Königslöw; Gregor Wiedemann (CoRR, 2023)

Argument Mining of Attack and Support Patterns in Dialogical Conversations with Sequential Pattern Mining. - Mattes Ruckdeschel; Ringo Baumann; Gregor Wiedemann (RATIO, 2024)

PETapter: Leveraging PET-style classification heads for modular few-shot parameter-efficient fine-tuning. - Jonas Rieger; Mattes Ruckdeschel; Gregor Wiedemann (CoRR, 2024)

Just Read the Codebook! Make Use of Quality Codebooks in Zero-Shot Classification of Multilabel Frame Datasets. - Mattes Ruckdeschel (COLING, 2025)