The Argument Reasoning Comprehension Task: Identification and Reconstruction of Implicit Warrants - Habernal, Ivan; Wachsmuth, Henning; Gurevych, Iryna; Stein, Benno (Proceedings of the 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies)

The Impact of Modeling Overall Argumentation with Tree Kernels - Wachsmuth, Henning; Da San Martino, Giovanni; Kiesel, Dora; Stein, Benno (Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing)

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)

The Moral Debater: A Study on the Computational Generation of Morally Framed Arguments - Alshomary, Milad; El Baff, Roxanne; Gurcke, Timon; Wachsmuth, Henning (Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics)

To Prefer or to Choose? Generating Agency and Power Counterfactuals Jointly for Gender Bias Mitigation - Stahl, Maja; Spliethöver, Maximilian; Wachsmuth, Henning (Proceedings of the Fifth Workshop on Natural Language Processing and Computational Social Science)
Gender bias may emerge from an unequal representation of agency and power, for example, by portraying women frequently as passive and powerless ("She accepted her future'') and men as proactive and powerful ("He chose his future''). When language models learn from respective texts, they may reproduce or even amplify the bias. An effective way to mitigate bias is to generate counterfactual sentences with opposite agency and power to the training. Recent work targeted agency-specific verbs from a lexicon to this end. We argue that this is insufficient, due to the interaction of agency and power and their dependence on context. In this paper, we thus develop a new rewriting model that identifies verbs with the desired agency and power in the context of the given sentence. The verbs' probability is then boosted to encourage the model to rewrite both connotations jointly. According to automatic metrics, our model effectively controls for power while being competitive in agency to the state of the art. In our evaluation, human annotators favored its counterfactuals in terms of both connotations, also deeming its meaning preservation better.

Topic-based Stance Mining for Social Media Texts - Chen, Wei-Fan; Lee, Yann-Hui; Ku, Lun-Wei (International Conference on HCI in Business)

Touché: First Shared Task on Argument Retrieval - Bondarenko, Alexander; Hagen, Matthias; Potthast, Martin; Wachsmuth, Henning; Beloucif, Meriem; Biemann, Chris; Panchenko, Alexander; Stein, Benno (Proceedings of the 42nd European Conference on Information Retrieval (ECIR 2020))

Toward Audience-aware Argument Generation - Alshomary, Milad; Wachsmuth, Henning (Patterns)

Unit Segmentation of Argumentative Texts - Ajjour, Yamen; Chen, Wei-Fan; Kiesel, Johannes; Wachsmuth, Henning; Stein, Benno (Proceedings of the 4th Workshop on Argument Mining)

Unraveling the Search Space of Abusive Language in Wikipedia with Dynamic Lexicon Acquisition - Chen, Wei-Fan; Al-Khatib, Khalid; Hagen, Matthias; Wachsmuth, Henning; Stein, Benno (Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom)