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

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.

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.

The Moral Debater: A Study on the Computational Generation of Morally Framed Arguments
M. Alshomary, R. El Baff, T. Gurcke, H. Wachsmuth, in: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, 2022, pp. 8782–8797.

"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.

Back to the Roots: Predicting the Source Domain of Metaphors using Contrastive Learning
M. Sengupta, M. Alshomary, H. Wachsmuth, in: Proceedings of the 2022 Workshop on Figurative Language Processing, 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.

Generating Contrastive Snippets for Argument Search
M. Alshomary, J. Rieskamp, H. Wachsmuth, in: Proceedings of the 9th International Conference on Computational Models of Argument, 2022, pp. 21–31.

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

Knowledge Base Enhanced & User-centric Dialogue Design for OTF Computing
M. Ahmed, Knowledge Base Enhanced &; User-Centric Dialogue Design for OTF Computing, 2022.

Domain-aware Text Professionalization using Sequence-to-Sequence Neural Networks
J. Palushi, Domain-Aware Text Professionalization Using Sequence-to-Sequence Neural Networks, 2022.

Propaganda Technique Detection Using Connotation Frames
V. Budanurmath, Propaganda Technique Detection Using Connotation Frames, 2022.

Learning From Revisions: Quality Assessment of Claims in Argumentation at Scale
G. Skitalinskaya, J. Klaff, H. Wachsmuth, in: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics, 2021, pp. 1718–1729.

Overview of Touché 2021: Argument Retrieval
A. Bondarenko, L. Gienapp, M. Fröbe, M. Beloucif, Y. Ajjour, A. Panchenko, C. Biemann, B. Stein, H. Wachsmuth, M. Potthast, M. Hagen, in: Proceedings of the 43rd Annual European Conference on Information Retrieval Research, 2021, pp. 384–395.