Computational Argumentation

NLP research on computational argumentation studies the analysis and synthesis of natural language arguments, usually in an empirical data-driven manner. The NLP Group is internationally known for its contributions to aspects of computational argumentation. Recent work focuses on how to make  large language models (LLMs)  more argumentative, while the group has earlier been involved in several groundbreaking publications on argument quality, generation, search, and more.

• Ziegenbein et al. (2026). Timon Ziegenbein,  Maja Stahl, and Henning Wachsmuth. Teaching LLMs Human-Like Editing of Inappropriate Argumentation via Reinforcement Learning. ACL 2026, to appear.
• Ajjour et al. (2026).  Yamen Ajjour, Carlotta Quensel, Nedim Lipke, and Henning Wachsmuth. ArgBench: Benchmarking LLMs on Computational Argumentation Tasks. ACL Findings 2026, to appear.
• Stahl et al. (2025). Maja Stahl, Timon Ziegenbein, Joonsuk Park, and Henning Wachsmuth. ArgInstruct: Specialized Instruction Fine-Tuning for Computational Argumentation. ACL Findings 2025.
• Musi et al. (2025). Elena Musi, Nadin Kökciyan, Khalid Al Khatib, Davide Ceolin, Emmanuelle Dietz, Klara Maximiliane Gutekunst, Annette Hautli-Janisz, Cristián Santibáñez, Jodi Schneider, Jonas Scholz, Cor Steging, Jacky Visser, and Henning Wachsmuth. Toward Reasonable Parrots: Why Large Language Models Should Argue with Us by Design. ArgMining 2025. **Honorable Mention for Best Paper Award**
• Wachsmuth et al. (2024).  Henning Wachsmuth, Gabriella Lapesa, Elena Cabrio, Anne Lauscher, Joonsuk Park, Eva Maria Vecchi, Serena Villata, and Timon Ziegenbein. Argument Quality Assessment in the Age of Instruction-Following Large Language Models. COLING 2024.

• Skitalinskaya et al. (2023). Gabriella Skitalinskaya, Maximilian Spliethöver, and Henning Wachsmuth. 2023. Claim Optimization in Computational Argumentation. INLG 2023. **Honorable Mention for Best Paper Award**
• Kiesel et al. (2022).  Johannes Kiesel, Milad Alshomary, Nicolas Handke, Xiaoni Cai, Henning Wachsmuth, and Benno Stein. 2022. Identifying the Human Values behind Arguments. ACL 2022.
• Alshomary et al. (2021). Milad Alshomary, Wei-Fan Chen, Timon Gurcke, and Henning Wachsmuth. 2021. Belief-based Generation of Argumentative Claims. EACL 2021.
• Spliethöver et al. (2020). Maximilian Spliethöver and Henning Wachsmuth. 2020. Argument from Old Man’s View: Assessing Social Bias in Argumentation. ArgMining 2020. **Best Paper Award**
• Ajjour et al. (2019). Yamen Ajjour, Milad Alshomary, Henning Wachsmuth, and Benno Stein. 2019. Modeling Frames in Argumentation. EMNLP 2019.
• Wachsmuth et al. (2018). Henning Wachsmuth, Shahbaz Syed, and Benno Stein. 2018. Retrieval of the Best Counterargument without Prior Topic Knowledge. ACL 2018.
• Wachsmuth et al. (2017b). Henning Wachsmuth, Martin Potthast, Khalid Al-Khatib, Yamen Ajjour, Jana Puschmann, Jiani Qu, Jonas Dorsch, Viorel Morari, Janek Bevendorff, and Benno Stein. 2017. Building an Argument Search Engine for the Web. ArgMining 2017.
• Wachsmuth et al. (2017a). Henning Wachsmuth, Nona Naderi, Yufang Hou, Yonatan Bilu, Vinodkumar Prabhakaran, Tim Alberdingk Thijm, Graeme Hirst, and Benno Stein. 2017. Computational Argumentation Quality Assessment in Natural Language. EACL 2017.
• Wachsmuth et al. (2016). Henning Wachsmuth, Khalid Al-Khatib, and Benno Stein. 2016. Using Argument Mining to Assess the Argumentation Quality of Essays. COLING 2016.
• Wachsmuth et al. (2015). Henning Wachsmuth, Johannes Kiesel, and Benno Stein. 2015. Sentiment Flow - A General Model of Web Review Argumentation. EMNLP 2015.

• Alshomary (2023): Milad Alshomary. Audience-Aware Argument Generation. December 20, 2023. **Dissertation Award of Paderborn University**
• Skitalinska (2023): Gabriella Skitalinska. Learning to Improve Arguments: Automated Claim Quality Assessment and Optimization. December, 13, 2023. 

• DFG Project ArgSchool (2021–2025): Computational Support for Learning Argumentative Writing in Digital School Education
• DFG Project OASiS (2021–2025): Objective Argument Summarization in Search