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.

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.

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.

M. Spliethöver and H. Wachsmuth, “Bias Silhouette Analysis: Towards Assessing the Quality of Bias Metrics for Word Embedding Models,”10.24963/ijcai.2021/77__in Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21, Online, 2021, pp. 552–559, doi:10.24963/ijcai.2021/77 .

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.

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.

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.

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.

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.

Z. Nouri, N. Prakash, U. Gadiraju, and H. Wachsmuth, Clarify - A Tool to Help Requesters Iteratively Improve Task Descriptions in Crowdsourcing,” 2021.

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.

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.

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.

M. Alshomary, S. Syed, M. Potthast, and H. Wachsmuth, “Argument Undermining: Counter-Argument Generation by Attacking Weak Premises,”10.18653/v1/2021.findings-acl.159__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 .

J. Bülling, Political Speaker Transfer: Learning to Generate Text in the Styles of Barack Obama and Donald Trump. 2021.

A. Mishra, Computational Text Professionalization using Neural Sequence-to-Sequence Models. 2021.