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88 Publications
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2023 | Conference Paper | LibreCat-ID: 34083
M. Stahl and H. Wachsmuth, “Identifying Feedback Types to Augment Feedback Comment Generation,” presented at the 16th International Natural Language Generation Conference.
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2023 | Book | LibreCat-ID: 45863 | 
C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, and H. Wehrheim, On-The-Fly Computing -- Individualized IT-services in dynamic markets, vol. 412. Paderborn: Heinz Nixdorf Institut, Universität Paderborn, 2023.
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2022 | Conference Paper | LibreCat-ID: 30840
M. Alshomary, R. El Baff, T. Gurcke, and H. Wachsmuth, “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, pp. 8782–8797.
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2022 | Conference Paper | LibreCat-ID: 33004
H. Wachsmuth and M. Alshomary, “‘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, pp. 344–354.
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2022 | Journal Article | LibreCat-ID: 34049
A. Lauscher, H. Wachsmuth, I. Gurevych, and G. Glavaš, “On the Role of Knowledge in Computational Argumentation,” Transactions of the Association for Computational Linguistics, 2022.
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2022 | Conference Paper | LibreCat-ID: 22157
J. Kiesel, M. Alshomary, N. Handke, X. Cai, H. Wachsmuth, and B. Stein, “Identifying the Human Values behind Arguments,” in Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, 2022, pp. 4459–4471.
LibreCat
2022 | Conference Paper | LibreCat-ID: 34067
M. Sengupta, M. Alshomary, and H. Wachsmuth, “Back to the Roots: Predicting the Source Domain of Metaphors using Contrastive Learning,” 2022.
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2022 | Conference Paper | LibreCat-ID: 34082
M. Stahl, M. Spliethöver, and H. Wachsmuth, “To Prefer or to Choose? Generating Agency and Power Counterfactuals Jointly for Gender Bias Mitigation,” presented at the Fifth Workshop on NLP and Computational Social Science (NLP+CSS) At EMNLP 2022, Abu Dhabi, United Arab Emirates.
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2022 | Conference Paper | LibreCat-ID: 33274
W.-F. Chen, M.-H. Chen, G. Mudgal, and H. Wachsmuth, “Analyzing Culture-Specific Argument Structures in Learner Essays,” in Proceedings of the 9th Workshop on Argument Mining (ArgMining 2022), 2022, pp. 51–61.
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2022 | Conference Paper | LibreCat-ID: 32247
M. Alshomary, J. Rieskamp, and H. Wachsmuth, “Generating Contrastive Snippets for Argument Search,” in Proceedings of the 9th International Conference on Computational Models of Argument, 2022, pp. 21–31, doi: http://dx.doi.org/10.3233/FAIA220138.
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2022 | Conference Abstract | LibreCat-ID: 31068
M.-H. Chen, G. Mudgal, W.-F. Chen, and H. Wachsmuth, “Investigating the argumentation structures of EFL learners from diverse language backgrounds,” 2022.
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2021 | Conference Paper | LibreCat-ID: 20115
G. Skitalinskaya, J. Klaff, and H. Wachsmuth, “Learning From Revisions: Quality Assessment of Claims in Argumentation at Scale,” in Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics, 2021, pp. 1718–1729.
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2021 | Conference Paper | LibreCat-ID: 3774
A. Bondarenko et al., “Overview of Touché 2021: Argument Retrieval,” in Proceedings of the 43rd annual European Conference on Information Retrieval Research, 2021, pp. 384–395.
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2021 | Conference Paper | LibreCat-ID: 23708 |
Z. Nouri, U. Gadiraju, G. Engels, and H. Wachsmuth, “What Is Unclear? Computational Assessment of Task Clarity in Crowdsourcing,” in Proceedings of the 32nd ACM Conference on Hypertext and Social Media, 2021, pp. 165–175.
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2021 | Conference Paper | LibreCat-ID: 22156 |
M. Spliethöver and H. Wachsmuth, “Bias Silhouette Analysis: Towards Assessing the Quality of Bias Metrics for Word Embedding Models,” in Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21, Online, 2021, pp. 552–559, doi: 10.24963/ijcai.2021/77.
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2021 | Conference Paper | LibreCat-ID: 22158 |
S. Syed, K. Al-Khatib, M. Alshomary, H. Wachsmuth, and M. Potthast, “Generating Informative Conclusions for Argumentative Texts,” in Proceedings of the Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021): Findings, 2021, pp. 3482–3493.
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2021 | Conference Paper | LibreCat-ID: 22159 |
J. Barrow et al., “Syntopical Graphs for Computational Argumentation Tasks,” in Proceedings of the Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021), 2021, pp. 1583–1595.
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2021 | Conference Paper | LibreCat-ID: 22160
K. Al-Khatib, L. Trautner, H. Wachsmuth, Y. Hou, and B. Stein, “Employing Argumentation Knowledge Graphs for Neural Argument Generation,” in Proceedings of the Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021), 2021, pp. 4744–4754.
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2021 | Conference Paper | LibreCat-ID: 22448
J. Kiesel, D. Spina, H. Wachsmuth, and B. Stein, “The Meant, the Said, and the Understood: Conversational Argument Search and Cognitive Biases,” in Proceedings of the 2021 Conversational User Interfaces Conference, 2021, pp. 1–5.
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2021 | Journal Article | LibreCat-ID: 22449
M. Alshomary and H. Wachsmuth, “Toward Audience-aware Argument Generation,” Patterns, vol. 2, no. 6, 2021.
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