Henning Wachsmuth
Prof. Dr. rer. nat. Henning Wachsmuth
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Welfengarten 1
30167 Hannover
30167 Hannover
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Prof. Dr. rer. nat. Henning Wachsmuth
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Publications
Showing results 1 - 20 out of 117
Chen WF, Alshomary M, Stahl M, Al Khatib K, Stein B, Wachsmuth H. Reference-guided Style-Consistent Content Transfer. In Calzolari N, Kan MY, Hoste V, Lenci A, Sakti S, Xue N, editors, 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC-COLING 2024 - Main Conference Proceedings. European Language Resources Association (ELRA). 2024. p. 13754-13768. (2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC-COLING 2024 - Main Conference Proceedings).
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Faggioli G, Dietz L, Clarke CLA, Demartini G, Hagen M, Hauff C et al. Who Determines What Is Relevant? Humans or AI? Why Not Both? A spectrum of human–artificial intelligence collaboration in assessing relevance. Communications of the ACM. 2024 Mar 25;67(4):31-34. Epub 2024 Mar 15. doi: 10.1145/3624730
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Sengupta M, El Baff R, Alshomary M, Wachsmuth H. Analyzing the Use of Metaphors in News Editorials for Political Framing. In Analyzing the Use of Metaphors in News Editorials for Political Framing. Mexico City, Mexico. 2024. p. 3621–3631
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Stahl M, Michel N, Kilsbach S, Schmidtke J, Rezat S, Wachsmuth H. A School Student Essay Corpus for Analyzing Interactions of Argumentative Structure and Quality. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024
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Stahl M, Biermann L, Nehring A, Wachsmuth H. Exploring LLM Prompting Strategies for Joint Essay Scoring and Feedback Generation. In Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2024). 2024
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Tornede A, Deng D, Eimer T, Giovanelli J, Mohan A, Ruhkopf T et al. AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks. Transactions on Machine Learning Research. 2024 Feb 9. Epub 2024 Feb 9. doi: 10.48550/arXiv.2306.08107
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Alshomary M, Wachsmuth H. Conclusion-based Counter-Argument Generation. In EACL 2023 - 17th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference. Association for Computational Linguistics (ACL). 2023. p. 957-967 Epub 2023 Jan 24. doi: 10.48550/arXiv.2301.09911
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Bäumer F, Chen WF, Geierhos M, Kersting J, Wachsmuth H. Dialogue-Based Requirement Compensation and Style-Adjusted Data-To-Text Generation. In On-The-Fly Computing : Individualized IT-Services in dynamic markets. 2023. p. 65-84 doi: 10.5281/zenodo.8068456
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Faggioli G, Clarke CLA, Demartini G, Hagen M, Hauff C, Kando N et al. Perspectives on Large Language Models for Relevance Judgment. In ICTIR '23: Proceedings of the 2023 ACM SIGIR International Conference on Theory of Information Retrieval. Association for Computing Machinery, Inc. 2023. p. 39-50 doi: 10.48550/arXiv.2304.09161, 10.1145/3578337.3605136
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Haake CJ, Auf Der Heide FM, Platzner M, Wachsmuth H, Wehrheim H. On-The-Fly Computing: Individualized IT-Services in dynamic markets. Paderborn: Verlagschriftenreihe des Heinz Nixdorf Instituts, 2023. (Verlagsschriftenreihe des Heinz Nixdorf Instituts). doi: 10.17619/UNIPB/1-1797
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Kiesel J, Alshomary M, Mirzakhmedova N, Heinrich M, Handke N, Wachsmuth H et al. SemEval-2023 Task 4: ValueEval: Identification of Human Values Behind Arguments. In Ojha AK, Doğruöz AS, Da San Martino G, Madabushi HT, Kumar R, Sartori E, editors, Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023). Association for Computational Linguistics (ACL). 2023. p. 2287-2303 doi: 10.18653/V1/2023.SEMEVAL-1.313
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Lapesa G, Vecchi EM, Villata S, Wachsmuth H. Mining, Assessing, and Improving Arguments in NLP and the Social Sciences. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: Tutorial Abstracts. Association for Computational Linguistics (ACL). 2023 doi: 10.18653/v1/2023.eacl-tutorials.1
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Nouri Z, Prakash N, Gadiraju U, Wachsmuth H. Supporting Requesters in Writing Clear Crowdsourcing Task Descriptions Through Computational Flaw Assessment. In IUI 2023 - Proceedings of the 28th International Conference on Intelligent User Interfaces. New York, NY, USA: Association for Computing Machinery (ACM). 2023. p. 737–749 doi: 10.1145/3581641.3584039
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Skitalinskaya G, Spliethöver M, Wachsmuth H. Claim Optimization in Computational Argumentation. In Keet CM, Lee HY, Zarrieß S, editors, Proceedings of the 16th International Natural Language Generation Conference. Association for Computational Linguistics (ACL). 2023. p. 134-152 doi: 10.48550/arXiv.2212.08913, 10.18653/v1/2023.inlg-main.10
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Skitalinskaya G, Wachsmuth H. To Revise or Not to Revise: Learning to Detect Improvable Claims for Argumentative Writing Support. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics (ACL). 2023. p. 15799–15816. (Proceedings of the Annual Meeting of the Association for Computational Linguistics). doi: 10.18653/v1/2023.acl-long.880
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Stahl M, Wachsmuth H. Identifying Feedback Types to Augment Feedback Comment Generation. In Proceedings of the 16th International Natural Language Generation Conference: Generation Challenges. 2023. p. 31-36
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Stahl M, Düsterhus N, Chen MH, Wachsmuth H. Mind the Gap: Automated Corpus Creation for Enthymeme Detection and Reconstruction in Learner Arguments. In Findings of the Association for Computational Linguistics: EMNLP 2023. 2023. p. 4703-4717 doi: 10.48550/arXiv.2310.18098, 10.18653/v1/2023.findings-emnlp.312
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Syed S, Ziegenbein T, Heinisch P, Wachsmuth H, Potthast M. Frame-oriented Summarization of Argumentative Discussions. In Proceedings of the 24th Meeting of the Special Interest Group on Discourse and Dialogue. Association for Computational Linguistics (ACL). 2023. p. 114-129 doi: 10.18653/v1/2023.sigdial-1.10
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Ziegenbein T, Syed S, Lange F, Potthast M, Wachsmuth H. Modeling Appropriate Language in Argumentation. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics (ACL). 2023. p. 4344-4363. (Proceedings of the Annual Meeting of the Association for Computational Linguistics). doi: 10.18653/v1/2023.acl-long.238
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Alshomary M, Rieskamp J, Wachsmuth H. Generating Contrastive Snippets for Argument Search. In Toni F, Polberg S, Booth R, Caminada M, Kido H, editors, Computational Models of Argument: Proceedings of COMMA 2022. Amsterdam: IOS Press. 2022. p. 21-31. (Frontiers in Artificial Intelligence and Applications). doi: 10.3233/FAIA220138
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