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127 Publications
2024 | Conference Paper | LibreCat-ID: 55338
Sengupta, M., El Baff, R., Alshomary, M., & Wachsmuth, H. (2024). Analyzing the Use of Metaphors in News Editorials for Political Framing. 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) (pp. 3621–3631). Association for Computational Linguistics.
LibreCat
2024 | Conference Paper | LibreCat-ID: 55404
Alshomary, M., Lange, F., Booshehri, M., Sengupta, M., Cimiano, P., & Wachsmuth, H. (2024). Modeling the Quality of Dialogical Explanations. 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) (pp. 11523–11536). ELRA and ICCL.
LibreCat
2024 | Conference Paper | LibreCat-ID: 58722
Spliethöver, M., Menon, S. N., & Wachsmuth, H. (2024). Disentangling Dialect from Social Bias via Multitask Learning to Improve Fairness. In L.-W. Ku, A. Martins, & V. Srikumar (Eds.), Findings of the Association for Computational Linguistics: ACL 2024 (pp. 9294–9313). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.findings-acl.553
LibreCat
| DOI
2023 | Conference Paper | LibreCat-ID: 34083
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
2023 | Book | LibreCat-ID: 45863 | 
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
| Files available
| DOI
2023 | Conference Paper | LibreCat-ID: 55406
Sengupta, M., Alshomary, M., Scharlau, I., & Wachsmuth, H. (2023). Modeling Highlighting of Metaphors in Multitask Contrastive Learning Paradigms. In H. Bouamor, J. Pino, & K. Bali (Eds.), Findings of the Association for Computational Linguistics: EMNLP 2023 (pp. 4636–4659). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.findings-emnlp.308
LibreCat
| DOI
2023 | Book Chapter | LibreCat-ID: 45882 |
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
| Files available
| DOI
2023 | Conference Paper | LibreCat-ID: 58723
Alshomary, M., & Wachsmuth, H. (2023). Conclusion-based Counter-Argument Generation. In A. Vlachos & I. Augenstein (Eds.), Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics (pp. 957–967). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.eacl-main.67
LibreCat
| DOI
2022 | Conference Paper | LibreCat-ID: 33004
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
2022 | Journal Article | LibreCat-ID: 34049
Lauscher, A., Wachsmuth, H., Gurevych, I., & Glavaš, G. (2022). On the Role of Knowledge in Computational Argumentation. Transactions of the Association for Computational Linguistics.
LibreCat
2022 | Conference Paper | LibreCat-ID: 22157
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
2022 | Conference Paper | LibreCat-ID: 34047
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
| arXiv
2022 | Book Chapter | LibreCat-ID: 34077
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
| DOI
2022 | Conference Paper | LibreCat-ID: 34051
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
2022 | Conference Paper | LibreCat-ID: 34082
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
2022 | Conference Paper | LibreCat-ID: 33274
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
2022 | Conference Abstract | LibreCat-ID: 31068
Chen, M.-H., Mudgal, G., Chen, W.-F., & Wachsmuth, H. (2022). Investigating the argumentation structures of EFL learners from diverse language backgrounds. EUROCALL.
LibreCat
2022 | Conference Paper | LibreCat-ID: 55337
Wachsmuth, H., & Alshomary, M. (2022). “Mama Always Had a Way of Explaining Things So I Could Understand”: A Dialogue Corpus for Learning to Construct Explanations. In N. Calzolari, C.-R. Huang, H. Kim, J. Pustejovsky, L. Wanner, K.-S. Choi, P.-M. Ryu, H.-H. Chen, L. Donatelli, H. Ji, S. Kurohashi, P. Paggio, N. Xue, S. Kim, Y. Hahm, Z. He, T. K. Lee, E. Santus, F. Bond, & S.-H. Na (Eds.), Proceedings of the 29th International Conference on Computational Linguistics (pp. 344–354). International Committee on Computational Linguistics.
LibreCat
2022 | Conference Paper | LibreCat-ID: 34067
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
2022 | Mastersthesis | LibreCat-ID: 29000
Ahmed, M. (2022). Knowledge Base Enhanced & User-centric Dialogue Design for OTF Computing.
LibreCat
| Files available