Stahl, M., &; Wachsmuth, H. (n.d.). Identifying Feedback Types to Augment Feedback Comment Generation. Proceedings of the 16th International Natural Language Generation Conference. 16th International Natural Language Generation Conference.
LibreCat: /record/34083

Bäumer, F. S., Chen, W.-F., Geierhos, M., Kersting, J., &; Wachsmuth, H. (2023). Dialogue-based Requirement Compensation and Style-adjusted Data-to-text Generation. 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 (Vol. 412, pp. 65–84). Heinz Nixdorf Institut, Universität Paderborn.https://doi.org/10.5281/zenodo.8068456 
LibreCat: /record/45882
DOI: 10.5281/zenodo.8068456

Haake, C.-J., Meyer auf der Heide, F., Platzner, M., Wachsmuth, H., &; Wehrheim, H. (2023). On-The-Fly Computing -- Individualized IT-services in dynamic markets (Vol. 412). Heinz Nixdorf Institut, Universität Paderborn.https://doi.org/10.17619/UNIPB/1-1797 
LibreCat: /record/45863
DOI: 10.17619/UNIPB/1-1797

Alshomary, M., El Baff, R., Gurcke, T., &; Wachsmuth, H. (2022). The Moral Debater: A Study on the Computational Generation of Morally Framed Arguments. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, 8782–8797.
LibreCat: /record/30840

Wachsmuth, H., &; Alshomary, M. (2022). “Mama Always Had a Way of Explaining Things So I Could Understand”: A Dialogue Corpus for Learning How to Explain. Proceedings of the 29th International Conference on Computational Linguistics, 344–354.
LibreCat: /record/33004

Lauscher, A., Wachsmuth, H., Gurevych, I., &; Glavas, G. (2022). On the Role of Knowledge in  Computational Argumentation. Transactions of the Association for Computational Linguistics.
LibreCat: /record/34049

Kiesel, J., Alshomary, M., Handke, N., Cai, X., Wachsmuth, H., &; Stein, B. (2022). Identifying the Human Values behind Arguments. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, 4459–4471.
LibreCat: /record/22157

Spliethöver, M., Keiff, M., &; Wachsmuth, H. (2022). No Word Embedding Model Is Perfect: Evaluating the Representation  Accuracy for Social Bias in the Media. Proceedings of The 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP 2022). The 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP 2022), Abu Dhabi.
LibreCat: /record/34047
arXiv: 2211.03634

Sengupta, M., Alshomary, M., &; Wachsmuth, H. (2022). Back to the Roots: Predicting the Source Domain of Metaphors using Contrastive Learning. Proceedings of the 2022 Workshop on Figurative Language Processing.
LibreCat: /record/34067

Bondarenko, A., Fröbe, M., Kiesel, J., Syed, S., Gurcke, T., Beloucif, M., Panchenko, A., Biemann, C., Stein, B., Wachsmuth, H., Potthast, M., &; Hagen, M. (2022). Overview of Touché 2022: Argument Retrieval. In Lecture Notes in Computer Science. Springer International Publishing.https://doi.org/10.1007/978-3-030-99739-7_43 
LibreCat: /record/34077
DOI: 10.1007/978-3-030-99739-7_43

Alshomary, M., &; Stahl, M. (2022). Argument Novelty and Validity Assessment via Multitask and Transfer Learning. Proceedings of the 9th Workshop on Argument Mining, 111–114.
LibreCat: /record/34051

Stahl, M., Spliethöver, M., &; Wachsmuth, H. (n.d.). To Prefer or to Choose? Generating Agency and Power Counterfactuals Jointly for Gender Bias Mitigation. Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science. Fifth Workshop on NLP and Computational Social Science (NLP+CSS)  At EMNLP 2022, Abu Dhabi, United Arab Emirates.
LibreCat: /record/34082

Chen, W.-F., Chen, M.-H., Mudgal, G., &; Wachsmuth, H. (2022). Analyzing Culture-Specific Argument Structures in Learner Essays. Proceedings of the 9th Workshop on Argument Mining (ArgMining 2022), 51–61.
LibreCat: /record/33274

Alshomary, M., Rieskamp, J., &; Wachsmuth, H. (2022). Generating Contrastive Snippets for Argument Search. Proceedings of the 9th International Conference on Computational Models of Argument, 21–31.http://dx.doi.org/10.3233/FAIA220138 
LibreCat: /record/32247
DOI: http://dx.doi.org/10.3233/FAIA220138

Chen, M.-H., Mudgal, G., Chen, W.-F., &; Wachsmuth, H. (2022). Investigating the argumentation structures of EFL learners from diverse language backgrounds. EUROCALL.
LibreCat: /record/31068

Ahmed, M. (2022). Knowledge Base Enhanced &; User-centric Dialogue Design for OTF Computing.
LibreCat: /record/29000

Palushi, J. (2022). Domain-aware Text Professionalization using Sequence-to-Sequence Neural Networks.
LibreCat: /record/45790

Budanurmath, V. (2022). Propaganda Technique Detection Using Connotation Frames.
LibreCat: /record/45789

Skitalinskaya, G., Klaff, J., &; Wachsmuth, H. (2021). Learning From Revisions: Quality Assessment of Claims in Argumentation at Scale. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics (pp. 1718–1729).
LibreCat: /record/20115

Bondarenko, A., Gienapp, L., Fröbe, M., Beloucif, M., Ajjour, Y., Panchenko, A.,  Hagen, M. (2021). Overview of Touché 2021: Argument Retrieval. In Proceedings of the 43rd annual European Conference on Information Retrieval Research (pp. 384–395).
LibreCat: /record/3774