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109 Publications
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.
LibreCat
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.
LibreCat
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.
LibreCat
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: 34047
M. Spliethöver, M. Keiff, and H. Wachsmuth, “No Word Embedding Model Is Perfect: Evaluating the Representation Accuracy for Social Bias in the Media,” presented at the The 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP 2022), Abu Dhabi, 2022.
LibreCat
| arXiv
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.
LibreCat
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.
LibreCat
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.
LibreCat
| DOI
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.
LibreCat