Investigating Co-Constructive Behavior of Large Language Models in  Explanation Dialogues
L. Fichtel, M. Spliethöver, E. Hüllermeier, P. Jimenez, N. Klowait, S. Kopp, A.-C. Ngonga Ngomo, A. Robrecht, I. Scharlau, L. Terfloth, A.-L. Vollmer, H. Wachsmuth, in: Proceedings of the 26th Annual Meeting of the Special Interest Group on Discourse and Dialogue, Association for Computational Linguistics, Avignon, France, n.d.

Adaptive Prompting: Ad-hoc Prompt Composition for Social Bias Detection
M. Spliethöver, T. Knebler, F. Fumagalli, M. Muschalik, B. Hammer, E. Hüllermeier, H. Wachsmuth, in: L. Chiruzzo, A. Ritter, L. Wang (Eds.), Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), Association for Computational Linguistics, Albuquerque, New Mexico, 2025, pp. 2421–2449.

Investigating the Impact of Conceptual Metaphors on LLM-based NLI through Shapley Interactions
M. Sengupta, M. Muschalik, F. Fumagalli, B. Hammer, E. Hüllermeier, D. Ghosh, H. Wachsmuth, in: Accepted in Findings , EMNLP , 2025.

Analyzing the Use of Metaphors in News Editorials for Political Framing
M. Sengupta, R. El Baff, M. Alshomary, H. Wachsmuth, in: K. Duh, H. Gomez, S. Bethard (Eds.), Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), Association for Computational Linguistics, Mexico City, Mexico, 2024, pp. 3621–3631.

Disentangling Dialect from Social Bias via Multitask Learning to Improve Fairness
M. Spliethöver, S.N. Menon, H. Wachsmuth, in: L.-W. Ku, A. Martins, V. Srikumar (Eds.), Findings of the Association for Computational Linguistics: ACL 2024, Association for Computational Linguistics, Bangkok, Thailand, 2024, pp. 9294–9313.

Modeling the Quality of Dialogical Explanations
M. Alshomary, F. Lange, M. Booshehri, M. Sengupta, P. Cimiano, H. Wachsmuth, in: N. Calzolari, M.-Y. Kan, V. Hoste, A. Lenci, S. Sakti, N. Xue (Eds.), Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), ELRA and ICCL, Torino, Italia, 2024, pp. 11523–11536.

Identifying Feedback Types to Augment Feedback Comment Generation
M. Stahl, H. Wachsmuth, in: Proceedings of the 16th International Natural Language Generation Conference, n.d.

On-The-Fly Computing -- Individualized IT-services in dynamic markets
C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, H. Wehrheim, On-The-Fly Computing -- Individualized IT-Services in Dynamic Markets, Heinz Nixdorf Institut, Universität Paderborn, Paderborn, 2023.

Modeling Highlighting of Metaphors in Multitask Contrastive Learning Paradigms
M. Sengupta, M. Alshomary, I. Scharlau, H. Wachsmuth, in: H. Bouamor, J. Pino, K. Bali (Eds.), Findings of the Association for Computational Linguistics: EMNLP 2023, Association for Computational Linguistics, Singapore, 2023, pp. 4636–4659.

Dialogue-based Requirement Compensation and Style-adjusted Data-to-text Generation
F.S. Bäumer, W.-F. Chen, M. Geierhos, J. Kersting, H. Wachsmuth, in: C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, H. Wehrheim (Eds.), On-The-Fly Computing -- Individualized IT-Services in Dynamic Markets, Heinz Nixdorf Institut, Universität Paderborn, Paderborn, 2023, pp. 65–84.

Conclusion-based Counter-Argument Generation
M. Alshomary, H. Wachsmuth, in: A. Vlachos, I. Augenstein (Eds.), Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, Association for Computational Linguistics, Dubrovnik, Croatia, 2023, pp. 957–967.

"Mama Always Had a Way of Explaining Things So I Could Understand": A Dialogue Corpus for Learning How to Explain
H. Wachsmuth, M. Alshomary, in: Proceedings of the 29th International Conference on Computational Linguistics, 2022, pp. 344–354.

On the Role of Knowledge in  Computational Argumentation
A. Lauscher, H. Wachsmuth, I. Gurevych, G. Glavas, Transactions of the Association for Computational Linguistics (2022).

Identifying the Human Values behind Arguments
J. Kiesel, M. Alshomary, N. Handke, X. Cai, H. Wachsmuth, B. Stein, in: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, 2022, pp. 4459–4471.

No Word Embedding Model Is Perfect: Evaluating the Representation  Accuracy for Social Bias in the Media
M. Spliethöver, M. Keiff, H. Wachsmuth, in: Proceedings of The 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP 2022), Association for Computational Linguistics, 2022.

Overview of Touché 2022: Argument Retrieval
A. Bondarenko, M. Fröbe, J. Kiesel, S. Syed, T. Gurcke, M. Beloucif, A. Panchenko, C. Biemann, B. Stein, H. Wachsmuth, M. Potthast, M. Hagen, in: Lecture Notes in Computer Science, Springer International Publishing, Cham, 2022.

Argument Novelty and Validity Assessment via Multitask and Transfer Learning
M. Alshomary, M. Stahl, in: Proceedings of the 9th Workshop on Argument Mining, International Conference on Computational Linguistics, Online and in Gyeongju, Republic of Korea, 2022, pp. 111–114.

To Prefer or to Choose? Generating Agency and Power Counterfactuals Jointly for Gender Bias Mitigation
M. Stahl, M. Spliethöver, H. Wachsmuth, in: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science, Abu Dhabi, United Arab Emirates, n.d.

Analyzing Culture-Specific Argument Structures in Learner Essays
W.-F. Chen, M.-H. Chen, G. Mudgal, H. Wachsmuth, in: Proceedings of the 9th Workshop on Argument Mining (ArgMining 2022), 2022, pp. 51–61.

Investigating the argumentation structures of EFL learners from diverse language backgrounds
M.-H. Chen, G. Mudgal, W.-F. Chen, H. Wachsmuth, in: EUROCALL, 2022.