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
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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, Hrsg., Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023). Association for Computational Linguistics (ACL). 2023. S. 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 Klinger R, Okazaki N, Calzolari N, Kan MY, Hrsg., Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: Tutorial Abstracts. Torino, Italia: Association for Computational Linguistics (ACL). 2023. S. 26-32 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. in Keet CM, Lee HY, Zarrieß S, Hrsg., Proceedings of the 16th International Natural Language Generation Conference. Association for Computational Linguistics (ACL). 2023. S. 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. 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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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. S. 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. S. 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. 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. in CLEF 2022 Working Notes: Proceedings of the Working Notes of CLEF 2022 - Conference and Labs of the Evaluation Forum. 2022. S. 2867-2903. (CEUR Workshop Proceedings).
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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 WF, Chen MH, 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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Spliethöver M, Keiff M, Wachsmuth H. No Word Embedding Model Is Perfect: Evaluating the Representation Accuracy for Social Bias in the Media. in Proceedings of The 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP 2022). Association for Computational Linguistics. 2022. S. 2081-2093 Epub 2022 Nov 7. doi: 10.18653/v1/2022.findings-emnlp.152
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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. Abu Dhabi, United Arab Emirates: Association for Computational Linguistics (ACL). 2022. S. 39-51. (NLPCSS 2022 - 5th Workshop on Natural Language Processing and Computational Social Science ,NLP+CSS, Held at the 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022).
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