Henning Wachsmuth
Prof. Dr. rer. nat. Henning Wachsmuth
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30167 Hannover
30167 Hannover
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Prof. Dr. rer. nat. Henning Wachsmuth
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Publications
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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. S. 957-967 Epub 2023 Jan 24. doi: 10.48550/arXiv.2301.09911
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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. S. 39-50 doi: 10.48550/arXiv.2304.09161, 10.1145/3578337.3605136
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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. 2023. 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. S. 737–749 doi: 10.1145/3581641.3584039
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Skitalinskaya G, Spliethöver M, Wachsmuth H. Claim Optimization in Computational Argumentation. 2023. doi: 10.48550/arXiv.2212.08913
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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. S. 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. S. 31-36
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Syed S, Ziegenbein T, Heinisch P, Wachsmuth H, Potthast M. Frame-oriented Summarization of Argumentative Discussions. 2023.
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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. 2023 Jun 13. Epub 2023 Jun 13. doi: 10.48550/arXiv.2306.08107
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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. S. 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, Hrsg., Computational Models of Argument: Proceedings of COMMA 2022. Amsterdam: IOS Press. 2022. S. 21-31. (Frontiers in Artificial Intelligence and Applications). doi: 10.3233/FAIA220138
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Alshomary M, El Baff R, Gurcke T, Wachsmuth H. The Moral Debater: A Study on the Computational Generation of Morally Framed Arguments. in Muresan S, Nakov P, Villavicencio A, Hrsg., Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics Volume 1: Long Papers. Association for Computational Linguistics (ACL). 2022. S. 8782 - 8797. (Proceedings of the Annual Meeting of the Association for Computational Linguistics). doi: 10.48550/arXiv.2203.14563, 10.18653/v1/2022.acl-long.601
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Bondarenko A, Fröbe M, Kiesel J, Syed S, Gurcke T, Beloucif M et al. Overview of Touché 2022: Argument Retrieval. CEUR Workshop Proceedings. 2022;3180:2867-2903.
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Bondarenko A, Fröbe M, Kiesel J, Syed S, Gurcke T, Beloucif M et al. Overview of Touché 2022: Argument Retrieval: Argument Retrieval: Extended Abstract. in Hagen M, Verberne S, Macdonald C, Seifert C, Balog K, Nørvåg K, Setty V, Hrsg., Advances in Information Retrieval: 44th European Conference on IR Research, ECIR 2022, Proceedings. Part 2 Aufl. Springer Science and Business Media Deutschland GmbH. 2022. S. 339-346. (Lecture Notes in Computer Science). doi: 10.1007/978-3-030-99739-7_43
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Chen W-F, Chen M-H, Mudgal G, Wachsmuth H. Analyzing Culture-Specific Argument Structures in Learner Essays. in Lapesa G, Schneider J, Jo Y, Saha S, Hrsg., Proceedings of the 9th Workshop on Argument Mining. Association for Computational Linguistics (ACL). 2022. S. 51 - 61
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Kiesel J, Alshomary M, Handke N, Cai X, Wachsmuth H, Stein B. Identifying the Human Values behind Arguments. in Muresan S, Nakov P, Villavicencio A, Hrsg., Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics Volume 1: Long Papers. Association for Computational Linguistics (ACL). 2022. S. 4459 - 4471. (Proceedings of the Annual Meeting of the Association for Computational Linguistics). doi: 10.18653/v1/2022.acl-long.306
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Lauscher A, Wachsmuth H, Gurevych I, Glavaš G. On the Role of Knowledge in Computational Argumentation. 2022. Epub 2022. doi: 10.48550/arXiv.2107.00281
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Lauscher A, Wachsmuth H, Gurevych I, Glavaš G. Scientia Potentia Est—On the Role of Knowledge in Computational Argumentation. Transactions of the Association for Computational Linguistics. 2022 Dez 22;10(10):1392-1422. doi: 10.1162/tacl_a_00525
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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. Association for Computational Linguistics (ACL). 2022. S. 137-142
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