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88 Publications
2023 | Conference Paper | LibreCat-ID: 34083
M. Stahl and H. Wachsmuth, “Identifying Feedback Types to Augment Feedback Comment Generation,” presented at the 16th International Natural Language Generation Conference.
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2023 | Book | LibreCat-ID: 45863 | 
C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, and H. Wehrheim, On-The-Fly Computing -- Individualized IT-services in dynamic markets, vol. 412. Paderborn: Heinz Nixdorf Institut, Universität Paderborn, 2023.
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2022 | Conference Paper | LibreCat-ID: 30840
M. Alshomary, R. El Baff, T. Gurcke, and H. Wachsmuth, “The Moral Debater: A Study on the Computational Generation of Morally Framed Arguments,” in Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, 2022, pp. 8782–8797.
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2022 | Conference Paper | LibreCat-ID: 33004
H. Wachsmuth and M. Alshomary, “‘Mama Always Had a Way of Explaining Things So I Could Understand’: A Dialogue Corpus for Learning How to Explain,” in Proceedings of the 29th International Conference on Computational Linguistics, 2022, pp. 344–354.
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2022 | Journal Article | LibreCat-ID: 34049
A. Lauscher, H. Wachsmuth, I. Gurevych, and G. Glavaš, “On the Role of Knowledge in Computational Argumentation,” Transactions of the Association for Computational Linguistics, 2022.
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2022 | Conference Paper | LibreCat-ID: 22157
J. Kiesel, M. Alshomary, N. Handke, X. Cai, H. Wachsmuth, and B. Stein, “Identifying the Human Values behind Arguments,” in Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, 2022, pp. 4459–4471.
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2022 | Conference Paper | LibreCat-ID: 34067
M. Sengupta, M. Alshomary, and H. Wachsmuth, “Back to the Roots: Predicting the Source Domain of Metaphors using Contrastive Learning,” 2022.
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2022 | Conference Paper | LibreCat-ID: 34082
M. Stahl, M. Spliethöver, and H. Wachsmuth, “To Prefer or to Choose? Generating Agency and Power Counterfactuals Jointly for Gender Bias Mitigation,” presented at the Fifth Workshop on NLP and Computational Social Science (NLP+CSS) At EMNLP 2022, Abu Dhabi, United Arab Emirates.
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2022 | Conference Paper | LibreCat-ID: 33274
W.-F. Chen, M.-H. Chen, G. Mudgal, and H. Wachsmuth, “Analyzing Culture-Specific Argument Structures in Learner Essays,” in Proceedings of the 9th Workshop on Argument Mining (ArgMining 2022), 2022, pp. 51–61.
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2022 | Conference Paper | LibreCat-ID: 32247
M. Alshomary, J. Rieskamp, and H. Wachsmuth, “Generating Contrastive Snippets for Argument Search,” in Proceedings of the 9th International Conference on Computational Models of Argument, 2022, pp. 21–31, doi: http://dx.doi.org/10.3233/FAIA220138.
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2022 | Conference Abstract | LibreCat-ID: 31068
M.-H. Chen, G. Mudgal, W.-F. Chen, and H. Wachsmuth, “Investigating the argumentation structures of EFL learners from diverse language backgrounds,” 2022.
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2021 | Conference Paper | LibreCat-ID: 20115
G. Skitalinskaya, J. Klaff, and H. Wachsmuth, “Learning From Revisions: Quality Assessment of Claims in Argumentation at Scale,” in Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics, 2021, pp. 1718–1729.
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2021 | Conference Paper | LibreCat-ID: 3774
A. Bondarenko et al., “Overview of Touché 2021: Argument Retrieval,” in Proceedings of the 43rd annual European Conference on Information Retrieval Research, 2021, pp. 384–395.
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2021 | Conference Paper | LibreCat-ID: 23708 |
Z. Nouri, U. Gadiraju, G. Engels, and H. Wachsmuth, “What Is Unclear? Computational Assessment of Task Clarity in Crowdsourcing,” in Proceedings of the 32nd ACM Conference on Hypertext and Social Media, 2021, pp. 165–175.
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2021 | Conference Paper | LibreCat-ID: 22156 |
M. Spliethöver and H. Wachsmuth, “Bias Silhouette Analysis: Towards Assessing the Quality of Bias Metrics for Word Embedding Models,” in Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21, Online, 2021, pp. 552–559, doi: 10.24963/ijcai.2021/77.
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2021 | Conference Paper | LibreCat-ID: 22158 |
S. Syed, K. Al-Khatib, M. Alshomary, H. Wachsmuth, and M. Potthast, “Generating Informative Conclusions for Argumentative Texts,” in 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, 2021, pp. 3482–3493.
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2021 | Conference Paper | LibreCat-ID: 22159 |
J. Barrow et al., “Syntopical Graphs for Computational Argumentation Tasks,” in 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), 2021, pp. 1583–1595.
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2021 | Conference Paper | LibreCat-ID: 22160
K. Al-Khatib, L. Trautner, H. Wachsmuth, Y. Hou, and B. Stein, “Employing Argumentation Knowledge Graphs for Neural Argument Generation,” in 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), 2021, pp. 4744–4754.
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2021 | Conference Paper | LibreCat-ID: 22448
J. Kiesel, D. Spina, H. Wachsmuth, and B. Stein, “The Meant, the Said, and the Understood: Conversational Argument Search and Cognitive Biases,” in Proceedings of the 2021 Conversational User Interfaces Conference, 2021, pp. 1–5.
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2021 | Journal Article | LibreCat-ID: 22449
M. Alshomary and H. Wachsmuth, “Toward Audience-aware Argument Generation,” Patterns, vol. 2, no. 6, 2021.
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2021 | Conference Paper | LibreCat-ID: 25297
M. Alshomary et al., “Key Point Analysis via Contrastive Learning and Extractive Argument Summarization,” in Proceedings of the 8th Workshop on Argument Mining, 2021, pp. 184–189.
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2021 | Conference Paper | LibreCat-ID: 25294
Z. Nouri, N. Prakash, U. Gadiraju, and H. Wachsmuth, “iClarify - A Tool to Help Requesters Iteratively Improve Task Descriptions in Crowdsourcing,” 2021.
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2021 | Conference Paper | LibreCat-ID: 25295
T. Gurcke, M. Alshomary, and H. Wachsmuth, “Assessing the Sufficiency of Arguments through Conclusion Generation,” in Proceedings of the 8th Workshop on Argument Mining, 2021, pp. 67–77.
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2021 | Conference Paper | LibreCat-ID: 21178
M. Alshomary, W.-F. Chen, T. Gurcke, and H. Wachsmuth, “Belief-based Generation of Argumentative Claims,” in Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, Online, 2021, pp. 224–223.
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2021 | Conference Paper | LibreCat-ID: 23709
W.-F. Chen, K. Al Khatib, B. Stein, and H. Wachsmuth, “Controlled Neural Sentence-Level Reframing of News Articles,” in Findings of the Association for Computational Linguistics: EMNLP 2021, 2021, pp. 2683–2693.
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2021 | Conference Paper | LibreCat-ID: 22229
M. Alshomary, S. Syed, M. Potthast, and H. Wachsmuth, “Argument Undermining: Counter-Argument Generation by Attacking Weak Premises,” in 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), Online, 2021, pp. 1816–1827, doi: 10.18653/v1/2021.findings-acl.159.
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2021 | Journal Article | LibreCat-ID: 24456 |
K. J. Rohlfing et al., “Explanation as a Social Practice: Toward a Conceptual Framework for the Social Design of AI Systems,” IEEE Transactions on Cognitive and Developmental Systems, vol. 13, no. 3, pp. 717–728, 2021, doi: 10.1109/tcds.2020.3044366.
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2020 | Conference Paper | LibreCat-ID: 20116
Z. Nouri, H. Wachsmuth, and G. Engels, “Mining Crowdsourcing Problems from Discussion Forums of Workers,” in Proceedings of COLING 2020, the 28th International Conference on Computational Linguistics, Barcelona, Spain, pp. 6264–6276.
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2020 | Conference Paper | LibreCat-ID: 20122
R. El Baff, K. Al-Khatib, B. Stein, and H. Wachsmuth, “Persuasiveness of News Editorials depending on Ideology and Personality,” in Third Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media (PEOPLES 2020), 2020, pp. 29–40.
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2020 | Conference Paper | LibreCat-ID: 20139 |
M. Spliethöver and H. Wachsmuth, “Argument from Old Man’s View: Assessing Social Bias in Argumentation,” in Proceedings of the 7th Workshop on Argument Mining (ArgMining 2020), 2020, pp. 76–87.
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2020 | Conference Paper | LibreCat-ID: 20140
J. Dorsch and H. Wachsmuth, “Semi-Supervised Cleansing of Web Argument Corpora,” in Proceedings of the 7th Workshop on Argument Mining (ArgMining 2020), 2020, pp. 19–29.
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2020 | Conference Paper | LibreCat-ID: 20166
A. Bondarenko et al., “Overview of Touché 2020: Argument Retrieval,” in CEUR Workshop Proceedings, 2020, vol. 2696, pp. 384–395.
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2020 | Conference Paper | LibreCat-ID: 3800
H. Wachsmuth and T. Werner, “Intrinsic Quality Assessment of Arguments,” in Proceedings of COLING 2020, the 28th International Conference on Computational Linguistics, 2020, pp. 6739–6745.
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2020 | Conference Paper | LibreCat-ID: 3878
R. El Baff, H. Wachsmuth, K. Al-Khatib, and B. Stein, “Analyzing the Persuasive Effect of Style in News Editorial Argumentation,” in Proceedings of 58th Annual Meeting of the Association for Computational Linguistics, 2020, pp. 553–564.
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2020 | Conference Paper | LibreCat-ID: 7283
M. Alshomary, N. Düsterhus, and H. Wachsmuth, “Extractive Snippet Generation for Arguments,” in Proceedings of 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, Xi’an, China, 2020, pp. 1969–1972.
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2020 | Conference Paper | LibreCat-ID: 15820
K. Al-Khatib, Y. Hou, H. Wachsmuth, C. Jochim, F. Bonin, and B. Stein, “End-to-End Argumentation Knowledge Graph Construction,” in Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020), 2020, pp. 7367–7374.
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2020 | Conference Paper | LibreCat-ID: 15821
A. Bondarenko et al., “Touché: First Shared Task on Argument Retrieval,” in Proceedings of the 42nd European Conference on Information Retrieval (ECIR 2020), 2020, pp. 517–523.
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2020 | Conference Paper | LibreCat-ID: 15825
J. Kiesel, K. Lang, H. Wachsmuth, E. Hornecker, and B. Stein, “Investigating Expectations for Voice-based and Conversational Argument Search on the Web,” in Proceedings of the 2020 ACM SIGIR Conference on Human Information Interaction & Retrieval (CHIIR 2020), 2020, pp. 53–62.
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2020 | Journal Article | LibreCat-ID: 10330
D. Kiesel, P. Riehmann, H. Wachsmuth, B. Stein, and B. Fröhlich, “Visual Analysis of Argumentation in Essays,” IEEE Transactions of Visualization & Computer Graphics, vol. 27, no. 2, pp. 1139–1148.
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2020 | Conference Paper | LibreCat-ID: 3776
W.-F. Chen, K. Al-Khatib, H. Wachsmuth, and B. Stein, “Analyzing Political Bias and Unfairness in News Articles at Different Levels of Granularity,” in Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science, 2020, pp. 149–154.
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2020 | Conference Paper | LibreCat-ID: 20137
S. Syed, W.-F. Chen, M. Hagen, B. Stein, H. Wachsmuth, and M. Potthast, “Task Proposal: Abstractive Snippet Generation for Web Pages,” in Proceedings of the 13th International Conference on Natural Language Generation (INLG 2020), 2020, pp. 237–241.
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2020 | Conference Paper | LibreCat-ID: 3818
W.-F. Chen, K. Al-Khatib, B. Stein, and H. Wachsmuth, “Detecting Media Bias in News Articles using Gaussian Bias Distributions,” in Findings of the Association for Computational Linguistics: EMNLP 2020, 2020, pp. 4290–4300.
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2020 | Conference Paper | LibreCat-ID: 16868
M. Alshomary, S. Syed, M. Potthast, and H. Wachsmuth, “Target Inference in Argument Conclusion Generation,” in Proceedings of 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020), Seattle, USA, 2020, pp. 4334–4345.
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2020 | Conference Paper | LibreCat-ID: 20141 |
S. Heindorf, Y. Scholten, H. Wachsmuth, A.-C. Ngonga Ngomo, and M. Potthast, “CauseNet: Towards a Causality Graph Extracted from the Web,” in Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM 2020), 2020, pp. 3023–3030, doi: 10.1145/3340531.3412763.
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2019 | Conference Paper | LibreCat-ID: 11709
M. Potthast et al., “Argument Search: Assessing Argument Relevance,” in 42nd International ACM Conference on Research and Development in Information Retrieval (SIGIR 2019), 2019, pp. 1117–1120.
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2019 | Book Review | LibreCat-ID: 11713
H. Wachsmuth, “Book Review: Argumentation Mining,” Computational Linguistics, vol. 45, no. 3. ACL, pp. 603–606, 2019.
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2019 | Conference Paper | LibreCat-ID: 11714
Y. Ajjour, H. Wachsmuth, J. Kiesel, M. Potthast, M. Hagen, and B. Stein, “Data Acquisition for Argument Search: The args.me Corpus,” in Proceedings of the 42nd Edition of the German Conference on Artificial Intelligence, 2019, pp. 48–59.
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2019 | Conference Paper | LibreCat-ID: 12931
Y. Ajjour, M. Alshomary, H. Wachsmuth, and B. Stein, “Modeling Frames in Argumentation,” in Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, 2019, pp. 2915–2925.
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2019 | Conference (Editor) | LibreCat-ID: 15235
B. Stein and H. Wachsmuth, Eds., Proceedings of the 6th Workshop on Argument Mining. Florence, Italy: Association for Computational Linguistics, 2019.
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2019 | Conference Paper | LibreCat-ID: 13144
R. El Baff, H. Wachsmuth, K. Al-Khatib, M. Stede, and B. Stein, “Computational Argumentation Synthesis as a Language Modeling Task,” in Proceedings of the 12th International Conference on Natural Language Generation, Tokyo, Japan, 2019, pp. 54–64.
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