@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}}, } @misc{45788, author = {{Bülling, Jonas}}, title = {{{Political Speaker Transfer: Learning to Generate Text in the Styles of Barack Obama and Donald Trump}}}, year = {{2021}}, } @misc{45787, author = {{Mishra, Avishek}}, title = {{{Computational Text Professionalization using Neural Sequence-to-Sequence Models}}}, 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}}, } @inproceedings{20166, author = {{Bondarenko, Alexander and Fröbe, Maik and Beloucif, Meriem and Gienapp, Lukas and Ajjour, Yamen and Panchenko, Alexander and Biemann, Chris and Stein, Benno and Wachsmuth, Henning and Potthast, Martin and Hagen, Matthias}}, booktitle = {{CEUR Workshop Proceedings}}, pages = {{384--395}}, title = {{{Overview of Touché 2020: Argument Retrieval}}}, volume = {{2696}}, year = {{2020}}, } @inproceedings{3800, author = {{Wachsmuth, Henning and Werner, Till}}, booktitle = {{Proceedings of COLING 2020, the 28th International Conference on Computational Linguistics}}, pages = {{6739--6745}}, title = {{{Intrinsic Quality Assessment of Arguments}}}, year = {{2020}}, } @inproceedings{3878, author = {{El Baff, Roxanne and Wachsmuth, Henning and Al-Khatib, Khalid and Stein, Benno}}, booktitle = {{Proceedings of 58th Annual Meeting of the Association for Computational Linguistics}}, editor = {{Tsujii, Junichi and Hajic, Jan}}, pages = {{553--564}}, title = {{{Analyzing the Persuasive Effect of Style in News Editorial Argumentation}}}, year = {{2020}}, } @inproceedings{7283, author = {{Alshomary, Milad and Düsterhus, Nick and Wachsmuth, Henning}}, booktitle = {{Proceedings of 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval}}, location = {{Xi'an, China}}, pages = {{1969--1972}}, title = {{{Extractive Snippet Generation for Arguments}}}, year = {{2020}}, } @inproceedings{15820, author = {{Al-Khatib, Khalid and Hou, Yufang and Wachsmuth, Henning and Jochim, Charles and Bonin, Francesca and Stein, Benno}}, booktitle = {{Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020)}}, pages = {{7367 -- 7374}}, title = {{{End-to-End Argumentation Knowledge Graph Construction}}}, year = {{2020}}, } @inproceedings{15821, author = {{Bondarenko, Alexander and Hagen, Matthias and Potthast, Martin and Wachsmuth, Henning and Beloucif, Meriem and Biemann, Chris and Panchenko, Alexander and Stein, Benno}}, booktitle = {{Proceedings of the 42nd European Conference on Information Retrieval (ECIR 2020)}}, pages = {{517--523}}, title = {{{Touché: First Shared Task on Argument Retrieval}}}, year = {{2020}}, } @inproceedings{15825, author = {{Kiesel, Johannes and Lang, Kevin and Wachsmuth, Henning and Hornecker, Eva and Stein, Benno}}, booktitle = {{Proceedings of the 2020 ACM SIGIR Conference on Human Information Interaction & Retrieval (CHIIR 2020)}}, pages = {{53--62}}, title = {{{Investigating Expectations for Voice-based and Conversational Argument Search on the Web}}}, year = {{2020}}, } @article{10330, author = {{Kiesel, Dora and Riehmann, Patrick and Wachsmuth, Henning and Stein, Benno and Fröhlich, Bernd}}, journal = {{IEEE Transactions of Visualization & Computer Graphics}}, number = {{2}}, pages = {{1139--1148}}, title = {{{Visual Analysis of Argumentation in Essays}}}, volume = {{27}}, year = {{2020}}, } @inproceedings{3776, author = {{Chen, Wei-Fan and Al-Khatib, Khalid and Wachsmuth, Henning and Stein, Benno}}, booktitle = {{Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science}}, pages = {{149--154}}, title = {{{Analyzing Political Bias and Unfairness in News Articles at Different Levels of Granularity}}}, year = {{2020}}, } @inproceedings{20137, author = {{Syed, Shahbaz and Chen, Wei-Fan and Hagen, Matthias and Stein, Benno and Wachsmuth, Henning and Potthast, Martin}}, booktitle = {{Proceedings of the 13th International Conference on Natural Language Generation (INLG 2020)}}, pages = {{237--241}}, title = {{{Task Proposal: Abstractive Snippet Generation for Web Pages}}}, year = {{2020}}, } @inproceedings{3818, author = {{Chen, Wei-Fan and Al-Khatib, Khalid and Stein, Benno and Wachsmuth, Henning}}, booktitle = {{Findings of the Association for Computational Linguistics: EMNLP 2020}}, pages = {{4290--4300}}, title = {{{Detecting Media Bias in News Articles using Gaussian Bias Distributions}}}, year = {{2020}}, }