Kiesel, Johannes, et al. “Identifying the Human Values behind Arguments.” Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, 2022, pp. 4459–71.

Sengupta, Meghdut, et al. “Back to the Roots: Predicting the Source Domain of Metaphors Using Contrastive Learning.” Proceedings of the 2022 Workshop on Figurative Language Processing, 2022.

Stahl, Maja, et al. “To Prefer or to Choose? Generating Agency and Power Counterfactuals Jointly for Gender Bias Mitigation.” Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science.

Chen, Wei-Fan, et al. “Analyzing Culture-Specific Argument Structures in Learner Essays.” Proceedings of the 9th Workshop on Argument Mining (ArgMining 2022), 2022, pp. 51–61.

Alshomary, Milad, et al. “Generating Contrastive Snippets for Argument Search.”http://dx.doi.org/10.3233/FAIA220138__Proceedings of the 9th International Conference on Computational Models of Argument, 2022, pp. 21–31, doi:http://dx.doi.org/10.3233/FAIA220138.

Chen, Mei-Hua, et al. “Investigating the Argumentation Structures of EFL Learners from Diverse Language Backgrounds.” EUROCALL, 2022.

Skitalinskaya, Gabriella, et al. “Learning From Revisions: Quality Assessment of Claims in Argumentation at Scale.” Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics, 2021, pp. 1718–29.

Bondarenko, Alexander, et al. “Overview of Touché 2021: Argument Retrieval.” Proceedings of the 43rd Annual European Conference on Information Retrieval Research, 2021, pp. 384–95.

Nouri, Zahra, et al. “What Is Unclear? Computational Assessment of Task Clarity in Crowdsourcing.” Proceedings of the 32nd ACM Conference on Hypertext and Social Media, 2021, pp. 165–75.

Spliethöver, Maximilian, and Henning Wachsmuth. “Bias Silhouette Analysis: Towards Assessing the Quality of Bias Metrics for Word Embedding Models.”10.24963/ijcai.2021/77__Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21, 2021, pp. 552–59, doi:10.24963/ijcai.2021/77.

Syed, Shahbaz, et al. “Generating Informative Conclusions for Argumentative Texts.” 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. 3482–93.

Barrow, Joe, et al. “Syntopical Graphs for Computational Argumentation Tasks.” 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. 1583–95.

Al-Khatib, Khalid, et al. “Employing Argumentation Knowledge Graphs for Neural Argument Generation.” 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. 4744–54.

Kiesel, Johannes, et al. “The Meant, the Said, and the Understood: Conversational Argument Search and Cognitive Biases.” Proceedings of the 2021 Conversational User Interfaces Conference, 2021, pp. 1–5.

Alshomary, Milad, and Henning Wachsmuth. “Toward Audience-Aware Argument Generation.” Patterns, vol. 2, no. 6, 2021.

Alshomary, Milad, et al. “Key Point Analysis via Contrastive Learning and Extractive Argument Summarization.” Proceedings of the 8th Workshop on Argument Mining, 2021, pp. 184–89.

Nouri, Zahra, et al. “IClarify - A Tool to Help Requesters Iteratively Improve Task Descriptions in Crowdsourcing.” Proceedings of the Ninth AAAI Conference on Human Computation and Crowdsourcing, HCOMP 2021, 2021.

Gurcke, Timon, et al. “Assessing the Sufficiency of Arguments through Conclusion Generation.” Proceedings of the 8th Workshop on Argument Mining, 2021, pp. 67–77.

Alshomary, Milad, et al. “Belief-Based Generation of Argumentative Claims.” Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, Association for Computational Linguistics, 2021, pp. 224–223.

Chen, Wei-Fan, et al. “Controlled Neural Sentence-Level Reframing of News Articles.” Findings of the Association for Computational Linguistics: EMNLP 2021, 2021, pp. 2683–93.