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94 Publications
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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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2021 | Conference Paper | LibreCat-ID: 25297
M. Alshomary et al., “Key Point Analysis via Contrastive Learning and Extractive Argument Summarization,” in Proceedings of the 8th Workshop on Argument Mining, 2021, pp. 184–189.
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2021 | Conference Paper | LibreCat-ID: 25294
Z. Nouri, N. Prakash, U. Gadiraju, and H. Wachsmuth, “iClarify - A Tool to Help Requesters Iteratively Improve Task Descriptions in Crowdsourcing,” 2021.
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2021 | Conference Paper | LibreCat-ID: 25295
T. Gurcke, M. Alshomary, and H. Wachsmuth, “Assessing the Sufficiency of Arguments through Conclusion Generation,” in Proceedings of the 8th Workshop on Argument Mining, 2021, pp. 67–77.
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2021 | Conference Paper | LibreCat-ID: 21178
M. Alshomary, W.-F. Chen, T. Gurcke, and H. Wachsmuth, “Belief-based Generation of Argumentative Claims,” in Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, Online, 2021, pp. 224–223.
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2021 | Conference Paper | LibreCat-ID: 23709
W.-F. Chen, K. Al Khatib, B. Stein, and H. Wachsmuth, “Controlled Neural Sentence-Level Reframing of News Articles,” in Findings of the Association for Computational Linguistics: EMNLP 2021, 2021, pp. 2683–2693.
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2021 | Conference Paper | LibreCat-ID: 22229
M. Alshomary, S. Syed, M. Potthast, and H. Wachsmuth, “Argument Undermining: Counter-Argument Generation by Attacking Weak Premises,” 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), Online, 2021, pp. 1816–1827, doi: 10.18653/v1/2021.findings-acl.159.
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2021 | Journal Article | LibreCat-ID: 24456 |
K. J. Rohlfing et al., “Explanation as a Social Practice: Toward a Conceptual Framework for the Social Design of AI Systems,” IEEE Transactions on Cognitive and Developmental Systems, vol. 13, no. 3, pp. 717–728, 2021, doi: 10.1109/tcds.2020.3044366.
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2020 | Conference Paper | LibreCat-ID: 20116
Z. Nouri, H. Wachsmuth, and G. Engels, “Mining Crowdsourcing Problems from Discussion Forums of Workers,” in Proceedings of COLING 2020, the 28th International Conference on Computational Linguistics, Barcelona, Spain, pp. 6264–6276.
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2020 | Conference Paper | LibreCat-ID: 20122
R. El Baff, K. Al-Khatib, B. Stein, and H. Wachsmuth, “Persuasiveness of News Editorials depending on Ideology and Personality,” in Third Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media (PEOPLES 2020), 2020, pp. 29–40.
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2020 | Conference Paper | LibreCat-ID: 20139 |
M. Spliethöver and H. Wachsmuth, “Argument from Old Man’s View: Assessing Social Bias in Argumentation,” in Proceedings of the 7th Workshop on Argument Mining (ArgMining 2020), 2020, pp. 76–87.
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2020 | Conference Paper | LibreCat-ID: 20140
J. Dorsch and H. Wachsmuth, “Semi-Supervised Cleansing of Web Argument Corpora,” in Proceedings of the 7th Workshop on Argument Mining (ArgMining 2020), 2020, pp. 19–29.
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2020 | Conference Paper | LibreCat-ID: 20166
A. Bondarenko et al., “Overview of Touché 2020: Argument Retrieval,” in CEUR Workshop Proceedings, 2020, vol. 2696, pp. 384–395.
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2020 | Conference Paper | LibreCat-ID: 3800
H. Wachsmuth and T. Werner, “Intrinsic Quality Assessment of Arguments,” in Proceedings of COLING 2020, the 28th International Conference on Computational Linguistics, 2020, pp. 6739–6745.
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2020 | Conference Paper | LibreCat-ID: 3878
R. El Baff, H. Wachsmuth, K. Al-Khatib, and B. Stein, “Analyzing the Persuasive Effect of Style in News Editorial Argumentation,” in Proceedings of 58th Annual Meeting of the Association for Computational Linguistics, 2020, pp. 553–564.
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