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97 Publications
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2025 | Conference Paper | LibreCat-ID: 61190
Sengupta M, Muschalik M, Fumagalli F, et al. Investigating the Impact of Conceptual Metaphors on LLM-based NLI through Shapley Interactions. In: Accepted in Findings . EMNLP ; 2025.
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2025 | Conference Paper | LibreCat-ID: 61234 | 
Fichtel L, Spliethöver M, Hüllermeier E, et al. Investigating Co-Constructive Behavior of Large Language Models in Explanation Dialogues. In: Proceedings of the 26th Annual Meeting of the Special Interest Group on Discourse and Dialogue. Association for Computational Linguistics.
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2025 | Conference Paper | LibreCat-ID: 59856 |
Spliethöver M, Knebler T, Fumagalli F, et al. Adaptive Prompting: Ad-hoc Prompt Composition for Social Bias Detection. In: Chiruzzo L, Ritter A, Wang L, 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; 2025:2421–2449.
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2024 | Conference Paper | LibreCat-ID: 55338
Sengupta M, El Baff R, Alshomary M, Wachsmuth H. Analyzing the Use of Metaphors in News Editorials for Political Framing. In: Duh K, Gomez H, Bethard S, 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; 2024:3621–3631.
LibreCat
2024 | Conference Paper | LibreCat-ID: 55404
Alshomary M, Lange F, Booshehri M, Sengupta M, Cimiano P, Wachsmuth H. Modeling the Quality of Dialogical Explanations. In: Calzolari N, Kan M-Y, Hoste V, Lenci A, Sakti S, Xue N, eds. Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024). ELRA and ICCL; 2024:11523–11536.
LibreCat
2024 | Conference Paper | LibreCat-ID: 58722 |
Spliethöver M, Menon SN, Wachsmuth H. Disentangling Dialect from Social Bias via Multitask Learning to Improve Fairness. In: Ku L-W, Martins A, Srikumar V, eds. Findings of the Association for Computational Linguistics: ACL 2024. Association for Computational Linguistics; 2024:9294–9313. doi:10.18653/v1/2024.findings-acl.553
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2023 | Conference Paper | LibreCat-ID: 34083
Stahl M, Wachsmuth H. Identifying Feedback Types to Augment Feedback Comment Generation. In: Proceedings of the 16th International Natural Language Generation Conference.
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2023 | Book | LibreCat-ID: 45863 |
Haake C-J, Meyer auf der Heide F, Platzner M, Wachsmuth H, Wehrheim H. On-The-Fly Computing -- Individualized IT-Services in Dynamic Markets. Vol 412. Heinz Nixdorf Institut, Universität Paderborn; 2023. doi:10.17619/UNIPB/1-1797
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2023 | Conference Paper | LibreCat-ID: 55406
Sengupta M, Alshomary M, Scharlau I, Wachsmuth H. Modeling Highlighting of Metaphors in Multitask Contrastive Learning Paradigms. In: Bouamor H, Pino J, Bali K, eds. Findings of the Association for Computational Linguistics: EMNLP 2023. Association for Computational Linguistics; 2023:4636–4659. doi:10.18653/v1/2023.findings-emnlp.308
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2023 | Conference Paper | LibreCat-ID: 58723
Alshomary M, Wachsmuth H. Conclusion-based Counter-Argument Generation. In: Vlachos A, Augenstein I, eds. Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics. Association for Computational Linguistics; 2023:957–967. doi:10.18653/v1/2023.eacl-main.67
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2022 | Conference Paper | LibreCat-ID: 33004
Wachsmuth H, Alshomary M. “Mama Always Had a Way of Explaining Things So I Could Understand”: A Dialogue Corpus for Learning How to Explain. In: Proceedings of the 29th International Conference on Computational Linguistics. ; 2022:344-354.
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2022 | Journal Article | LibreCat-ID: 34049
Lauscher A, Wachsmuth H, Gurevych I, Glavaš G. On the Role of Knowledge in Computational Argumentation. Transactions of the Association for Computational Linguistics. Published online 2022.
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2022 | Conference Paper | LibreCat-ID: 22157
Kiesel J, Alshomary M, Handke N, Cai X, Wachsmuth H, Stein B. Identifying the Human Values behind Arguments. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics. ; 2022:4459-4471.
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2022 | Conference Paper | LibreCat-ID: 34082
Stahl M, Spliethöver M, Wachsmuth H. To Prefer or to Choose? Generating Agency and Power Counterfactuals Jointly for Gender Bias Mitigation. In: Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science.
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2022 | Conference Paper | LibreCat-ID: 33274
Chen W-F, Chen M-H, Mudgal G, Wachsmuth H. Analyzing Culture-Specific Argument Structures in Learner Essays. In: Proceedings of the 9th Workshop on Argument Mining (ArgMining 2022). ; 2022:51-61.
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2022 | Conference Abstract | LibreCat-ID: 31068
Chen M-H, Mudgal G, Chen W-F, Wachsmuth H. Investigating the argumentation structures of EFL learners from diverse language backgrounds. In: EUROCALL. ; 2022.
LibreCat
2022 | Conference Paper | LibreCat-ID: 55337
Wachsmuth H, Alshomary M. “Mama Always Had a Way of Explaining Things So I Could Understand”: A Dialogue Corpus for Learning to Construct Explanations. In: Calzolari N, Huang C-R, Kim H, et al., eds. Proceedings of the 29th International Conference on Computational Linguistics. International Committee on Computational Linguistics; 2022:344–354.
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2022 | Conference Paper | LibreCat-ID: 34067
Sengupta M, Alshomary M, Wachsmuth H. Back to the Roots: Predicting the Source Domain of Metaphors using Contrastive Learning. In: Proceedings of the 2022 Workshop on Figurative Language Processing. ; 2022.
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2022 | Conference Paper | LibreCat-ID: 32247
Alshomary M, Rieskamp J, Wachsmuth H. Generating Contrastive Snippets for Argument Search. In: Proceedings of the 9th International Conference on Computational Models of Argument. ; 2022:21-31. doi:http://dx.doi.org/10.3233/FAIA220138
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2022 | Conference Paper | LibreCat-ID: 30840
Alshomary M, El Baff R, Gurcke T, Wachsmuth H. The Moral Debater: A Study on the Computational Generation of Morally Framed Arguments. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics. ; 2022:8782-8797.
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