@inproceedings{20115, author = {{Skitalinskaya, Gabriella and Klaff, Jonas and Wachsmuth, Henning}}, booktitle = {{Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics}}, pages = {{1718--1729}}, title = {{{Learning From Revisions: Quality Assessment of Claims in Argumentation at Scale}}}, year = {{2021}}, } @inproceedings{3774, author = {{Bondarenko, Alexander and Gienapp, Lukas and Fröbe, Maik and Beloucif, Meriem and Ajjour, Yamen and Panchenko, Alexander and Biemann, Chris and Stein, Benno and Wachsmuth, Henning and Potthast, Martin and Hagen, Matthias}}, booktitle = {{Proceedings of the 43rd annual European Conference on Information Retrieval Research}}, pages = {{384--395}}, title = {{{Overview of Touché 2021: Argument Retrieval}}}, year = {{2021}}, } @inproceedings{23708, author = {{Nouri, Zahra and Gadiraju, Ujwal and Engels, Gregor and Wachsmuth, Henning}}, booktitle = {{Proceedings of the 32nd ACM Conference on Hypertext and Social Media}}, pages = {{165--175}}, title = {{{What Is Unclear? Computational Assessment of Task Clarity in Crowdsourcing}}}, year = {{2021}}, } @inproceedings{22156, abstract = {{Word embedding models reflect bias towards genders, ethnicities, and other social groups present in the underlying training data. Metrics such as ECT, RNSB, and WEAT quantify bias in these models based on predefined word lists representing social groups and bias-conveying concepts. How suitable these lists actually are to reveal bias - let alone the bias metrics in general - remains unclear, though. In this paper, we study how to assess the quality of bias metrics for word embedding models. In particular, we present a generic method, Bias Silhouette Analysis (BSA), that quantifies the accuracy and robustness of such a metric and of the word lists used. Given a biased and an unbiased reference embedding model, BSA applies the metric systematically for several subsets of the lists to the models. The variance and rate of convergence of the bias values of each model then entail the robustness of the word lists, whereas the distance between the models' values gives indications of the general accuracy of the metric with the word lists. We demonstrate the behavior of BSA on two standard embedding models for the three mentioned metrics with several word lists from existing research.}}, author = {{Spliethöver, Maximilian and Wachsmuth, Henning}}, booktitle = {{Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21}}, location = {{Online}}, pages = {{552--559}}, title = {{{Bias Silhouette Analysis: Towards Assessing the Quality of Bias Metrics for Word Embedding Models}}}, doi = {{10.24963/ijcai.2021/77}}, year = {{2021}}, } @inproceedings{22158, author = {{Syed, Shahbaz and Al-Khatib, Khalid and Alshomary, Milad and Wachsmuth, Henning and Potthast, Martin}}, booktitle = {{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}}, pages = {{3482--3493}}, title = {{{Generating Informative Conclusions for Argumentative Texts}}}, year = {{2021}}, } @inproceedings{22159, author = {{Barrow, Joe and Jain, Rajiv and Lipka, Nedim and Dernoncourt, Franck and Morariu, Vlad and Manjunatha, Varun and Oard, Douglas and Resnik, Philip and Wachsmuth, Henning}}, booktitle = {{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)}}, pages = {{1583--1595}}, title = {{{Syntopical Graphs for Computational Argumentation Tasks}}}, year = {{2021}}, } @inproceedings{22160, author = {{Al-Khatib, Khalid and Trautner, Lukas and Wachsmuth, Henning and Hou, Yufang and Stein, Benno}}, booktitle = {{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)}}, pages = {{4744--4754}}, title = {{{Employing Argumentation Knowledge Graphs for Neural Argument Generation}}}, year = {{2021}}, } @inproceedings{22448, author = {{Kiesel, Johannes and Spina, Damiano and Wachsmuth, Henning and Stein, Benno}}, booktitle = {{Proceedings of the 2021 Conversational User Interfaces Conference}}, pages = {{1--5}}, title = {{{The Meant, the Said, and the Understood: Conversational Argument Search and Cognitive Biases}}}, year = {{2021}}, } @article{22449, author = {{Alshomary, Milad and Wachsmuth, Henning}}, journal = {{Patterns}}, number = {{6}}, title = {{{Toward Audience-aware Argument Generation}}}, volume = {{2}}, year = {{2021}}, } @inproceedings{25297, author = {{Alshomary, Milad and Gurcke, Timon and Syed, Shahbaz and Heinisch, Philipp and Spliethöver, Maximilian and Cimiano, Philipp and Potthast, Martin and Wachsmuth, Henning}}, booktitle = {{Proceedings of the 8th Workshop on Argument Mining}}, pages = {{184 -- 189}}, title = {{{Key Point Analysis via Contrastive Learning and Extractive Argument Summarization}}}, year = {{2021}}, } @inproceedings{25294, author = {{Nouri, Zahra and Prakash, Nikhil and Gadiraju, Ujwal and Wachsmuth, Henning}}, booktitle = {{Proceedings of the Ninth AAAI Conference on Human Computation and Crowdsourcing, HCOMP 2021}}, title = {{{iClarify - A Tool to Help Requesters Iteratively Improve Task Descriptions in Crowdsourcing}}}, year = {{2021}}, } @inproceedings{25295, author = {{Gurcke, Timon and Alshomary, Milad and Wachsmuth, Henning}}, booktitle = {{Proceedings of the 8th Workshop on Argument Mining}}, pages = {{67 -- 77}}, title = {{{Assessing the Sufficiency of Arguments through Conclusion Generation}}}, year = {{2021}}, } @inproceedings{23709, author = {{Chen, Wei-Fan and Al Khatib, Khalid and Stein, Benno and Wachsmuth, Henning}}, booktitle = {{Findings of the Association for Computational Linguistics: EMNLP 2021}}, pages = {{2683 -- 2693}}, title = {{{Controlled Neural Sentence-Level Reframing of News Articles}}}, year = {{2021}}, } @inproceedings{22229, author = {{Alshomary, Milad and Syed, Shahbaz and Potthast, Martin and Wachsmuth, Henning}}, booktitle = {{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)}}, location = {{Online}}, pages = {{1816–1827}}, publisher = {{Association for Computational Linguistics}}, title = {{{Argument Undermining: Counter-Argument Generation by Attacking Weak Premises}}}, doi = {{10.18653/v1/2021.findings-acl.159}}, year = {{2021}}, } @article{24456, abstract = {{One objective of current research in explainable intelligent systems is to implement social aspects in order to increase the relevance of explanations. In this paper, we argue that a novel conceptual framework is needed to overcome shortcomings of existing AI systems with little attention to processes of interaction and learning. Drawing from research in interaction and development, we first outline the novel conceptual framework that pushes the design of AI systems toward true interactivity with an emphasis on the role of the partner and social relevance. We propose that AI systems will be able to provide a meaningful and relevant explanation only if the process of explaining is extended to active contribution of both partners that brings about dynamics that is modulated by different levels of analysis. Accordingly, our conceptual framework comprises monitoring and scaffolding as key concepts and claims that the process of explaining is not only modulated by the interaction between explainee and explainer but is embedded into a larger social context in which conventionalized and routinized behaviors are established. We discuss our conceptual framework in relation to the established objectives of transparency and autonomy that are raised for the design of explainable AI systems currently.}}, author = {{Rohlfing, Katharina J. and Cimiano, Philipp and Scharlau, Ingrid and Matzner, Tobias and Buhl, Heike M. and Buschmeier, Hendrik and Esposito, Elena and Grimminger, Angela and Hammer, Barbara and Haeb-Umbach, Reinhold and Horwath, Ilona and Hüllermeier, Eyke and Kern, Friederike and Kopp, Stefan and Thommes, Kirsten and Ngonga Ngomo, Axel-Cyrille and Schulte, Carsten and Wachsmuth, Henning and Wagner, Petra and Wrede, Britta}}, issn = {{2379-8920}}, journal = {{IEEE Transactions on Cognitive and Developmental Systems}}, keywords = {{Explainability, process ofexplaining andunderstanding, explainable artificial systems}}, number = {{3}}, pages = {{717--728}}, title = {{{Explanation as a Social Practice: Toward a Conceptual Framework for the Social Design of AI Systems}}}, doi = {{10.1109/tcds.2020.3044366}}, volume = {{13}}, year = {{2021}}, } @inproceedings{21178, abstract = {{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.}}, author = {{Alshomary, Milad and Chen, Wei-Fan and Gurcke, Timon and Wachsmuth, Henning}}, booktitle = {{Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume}}, location = {{Online}}, pages = {{224--233}}, publisher = {{Association for Computational Linguistics}}, title = {{{Belief-based Generation of Argumentative Claims}}}, doi = {{10.18653/v1/2021.eacl-main.17}}, year = {{2021}}, } @inproceedings{20116, author = {{Nouri, Zahra and Wachsmuth, Henning and Engels, Gregor}}, booktitle = {{Proceedings of COLING 2020, the 28th International Conference on Computational Linguistics}}, location = {{Barcelona, Spain}}, pages = {{6264--6276}}, title = {{{Mining Crowdsourcing Problems from Discussion Forums of Workers}}}, year = {{2020}}, } @inproceedings{20122, author = {{El Baff, Roxanne and Al-Khatib, Khalid and Stein, Benno and Wachsmuth, Henning}}, booktitle = {{Third Workshop on Computational Modeling of People's Opinions, Personality, and Emotions in Social Media (PEOPLES 2020)}}, pages = {{29--40}}, title = {{{Persuasiveness of News Editorials depending on Ideology and Personality}}}, year = {{2020}}, } @inproceedings{20139, author = {{Spliethöver, Maximilian and Wachsmuth, Henning}}, booktitle = {{Proceedings of the 7th Workshop on Argument Mining (ArgMining 2020)}}, pages = {{76--87}}, title = {{{Argument from Old Man's View: Assessing Social Bias in Argumentation}}}, year = {{2020}}, } @inproceedings{20140, author = {{Dorsch, Jonas and Wachsmuth, Henning}}, booktitle = {{Proceedings of the 7th Workshop on Argument Mining (ArgMining 2020)}}, pages = {{19--29}}, title = {{{Semi-Supervised Cleansing of Web Argument Corpora}}}, year = {{2020}}, }