Argumentationsanalyse für das Web
Zusammenfassung der Projektergebnisse
The project evolved into several pioneering and influential streams of research in the field of computational argumentation. First, we redefined and empirically validated a common view on argument quality and convincingness. This resulted not only in creating new large data benchmarks, but it also influenced the research community to take further steps in this direction. Second, we investigated common-sense reasoning in ordinary Web argumentation and coined a new challenge in argument reasoning comprehension. Despite its narrow focus, it attracted participants outside the argument mining community and helped in gaining visibility and dissemination. Third, we developed a common open-source platform for large-scale argument search: args.me has drawn great attention from both researchers and Internet users. The search engine is employed to help the users forming stances and making decisions on topical and crucial concerns in our society. Fourth, through the educational aspect of the serious game for fallacy recognition, we aimed at raising awareness of faulty argumentation in the general public. By organizing workshops (Argument Mining in 2017 and 2019), shared tasks (SemEval 2018 Task 12), providing all newly created datasets and source codes under permissive licenses, and dissemination by invited talks, we believe that we built a strong foundation for a successful collaborative and open research within the NLP community and beyond.
Projektbezogene Publikationen (Auswahl)
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Using Argument Mining to Assess the Argumentation Quality of Essays. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 1680–1691, Osaka, Japan, 2016. The COLING 2016 Organizing Committee
H. Wachsmuth, K. Al Khatib, and B. Stein
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What makes a convincing argument? Empirical analysis and detecting attributes of convincingness in Web argumentation. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 1214–1223, Austin, Texas, 2016. Association for Computational Linguistics
I. Habernal and I. Gurevych
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Argumentation Mining in User-Generated Web Discourse. Computational Linguistics, 43(1):125–179, 2017
I. Habernal and I. Gurevych
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Argumentation Quality Assessment: Theory vs. Practice. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 250–255, Vancouver, Canada, 2017. Association for Computational Linguistics
H. Wachsmuth, N. Naderi, I. Habernal, Y. Hou, G. Hirst, I. Gurevych, and B. Stein
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Computational Argumentation Quality Assessment in Natural Language. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 176–187, Valencia, Spain, 2017. Association for Computational Linguistics
H. Wachsmuth, N. Naderi, Y. Hou, Y. Bilu, V. Prabhakaran, T. A. Thijmm, G. Hirst, and B. Stein
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Before Name-calling: Dynamics and Triggers of Ad Hominem Fallacies in Web Argumentation. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 386–396, New Orleans, Louisiana, 2018. Association for Computational Linguistics
I. Habernal, H. Wachsmuth, I. Gurevych, and B. Stein
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Modeling Deliberative Argumentation Strategies on Wikipedia. In I. Gurevych and Y. Miyao, editors, 56th Annual Meeting of the Association for Computational Linguistics (ACL 2018), pages 2545–2555. Association for Computational Linguistics, July 2018
K. Al-Khatib, H. Wachsmuth, K. Lang, J. Herpel, M. Hagen, and B. Stein
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SemEval-2018 Task 12: The Argument Reasoning Comprehension Task. In Proceedings of The 12th International Workshop on Semantic Evaluation, pages 763–772, New Orleans, Louisiana, 2018. Association for Computational Linguistics
I. Habernal, H. Wachsmuth, I. Gurevych, and B. Stein
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The Argument Reasoning Comprehension Task: Identification and Reconstruction of Implicit Warrants. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 1930–1940. Association for Computational Linguistics, 2018
I. Habernal, H. Wachsmuth, I. Gurevych, and B. Stein
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End-to-End Argumentation Knowledge Graph Construction. In 34th AAAI Conference on Artificial Intelligence (AAAI 2020), pages 7367–7374. AAAI, Feb. 2020
K. Al-Khatib, Y. Hou, H. Wachsmuth, C. Jochim, F. Bonin, and B. Stein