The Meant, the Said, and the Understood: Conversational Argument Search and Cognitive Biases - Kiesel, Johannes; Spina, Damiano; Wachsmuth, Henning; Stein, Benno (Proceedings of the 2021 Conversational User Interfaces Conference)

Key Point Analysis via Contrastive Learning and Extractive Argument Summarization - Alshomary, Milad; Gurcke, Timon; Syed, Shahbaz; Heinisch, Philipp; Spliethöver, Maximilian; Cimiano, Philipp; Potthast, Martin; Wachsmuth, Henning (Proceedings of the 8th Workshop on Argument Mining)

iClarify - A Tool to Help Requesters Iteratively Improve Task Descriptions in Crowdsourcing - Nouri, Zahra; Prakash, Nikhil; Gadiraju, Ujwal; Wachsmuth, Henning (Proceedings of the Ninth AAAI Conference on Human Computation and Crowdsourcing, HCOMP 2021)

Assessing the Sufficiency of Arguments through Conclusion Generation - Gurcke, Timon; Alshomary, Milad; Wachsmuth, Henning (Proceedings of the 8th Workshop on Argument Mining)

Belief-based Generation of Argumentative Claims - Alshomary, Milad; Chen, Wei-Fan; Gurcke, Timon; Wachsmuth, Henning (Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume)
When engaging in argumentative discourse, skilled human debaters tailor claims to the beliefs of the audience, to construct effective arguments. Recently, the field of computational argumentation witnessed extensive effort to address the automatic generation of arguments. However, existing approaches do not perform any audience-specific adaptation. In this work, we aim to bridge this gap by studying the task of belief-based claim generation: Given a controversial topic and a set of beliefs, generate an argumentative claim tailored to the beliefs. To tackle this task, we model the people's prior beliefs through their stances on controversial topics and extend state-of-the-art text generation models to generate claims conditioned on the beliefs. Our automatic evaluation confirms the ability of our approach to adapt claims to a set of given beliefs. In a manual study, we additionally evaluate the generated claims in terms of informativeness and their likelihood to be uttered by someone with a respective belief. Our results reveal the limitations of modeling users' beliefs based on their stances, but demonstrate the potential of encoding beliefs into argumentative texts, laying the ground for future exploration of audience reach.

Controlled Neural Sentence-Level Reframing of News Articles - Chen, Wei-Fan; Al Khatib, Khalid; Stein, Benno; Wachsmuth, Henning (Findings of the Association for Computational Linguistics: EMNLP 2021)

Argument Undermining: Counter-Argument Generation by Attacking Weak Premises - Alshomary, Milad; Syed, Shahbaz; Potthast, Martin; Wachsmuth, Henning (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))

Political Speaker Transfer: Learning to Generate Text in the Styles of Barack Obama and Donald Trump - Bülling, Jonas

Computational Text Professionalization using Neural Sequence-to-Sequence Models - Mishra, Avishek

Mining Crowdsourcing Problems from Discussion Forums of Workers - Nouri, Zahra; Wachsmuth, Henning; Engels, Gregor (Proceedings of COLING 2020, the 28th International Conference on Computational Linguistics)