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.

H. Wachsmuth, B. Stein, and Y. Ajjour, “‘Page Rank’’’ for Argument Relevance,’” in Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, 2017, pp. 1117–1127.

W.-F. Chen, M.-H. Chen, M.-L. Chen, and L.-W. Ku, “A Computer-assistance Learning System for Emotional Wording,” IEEE Transactions on Knowledge and Data Engineering, vol. 28, no. 5, pp. 1093–1104, 2015.

K. Al Khatib, H. Wachsmuth, J. Kiesel, M. Hagen, and B. Stein, “A News Editorial Corpus for Mining Argumentation Strategies,” in Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, 2016, pp. 3433–3443.

M. Potthast, W.-F. Chen, M. Hagen, and B. Stein, “A Plan for Ancillary Copyright: Original Snippets.,” in Proceedings of the Second International Workshop on Recent Trends in News Information Retrieval, 2018, pp. 3–5.

H. Wachsmuth, M. Trenkmann, B. Stein, G. Engels, and T. Palakarska, “A Review Corpus for Argumentation Analysis,” in Proceedings of the 15th International Conference on Intelligent Text Processing and Computational Linguistics, 2014, pp. 115–127.

W.-F. Chen, C.-K. Kuo, Y.-R.  Wang, and S.-H.  Chen, “A Syllable-based Prosody Modeling for L1 and L2 English Speeches,” in Proceedings of the 8th International Symposium on Chinese Spoken Language Processing, 2012, pp. 281–285.

H. Wachsmuth and B. Stein, “A Universal Model for Discourse-Level Argumentation Analysis,” Special Section of the ACM Transactions on Internet Technology: Argumentation in Social Media, no. 3, pp. 1–24, 2017.

W.-F. Chen, M. Hagen, B. Stein, and M. Potthast, “A User Study on Snippet Generation: Text Reuse vs. Paraphrases,” in Proceedings of the 41st International ACM SIGIR Conference on Research &; Development in Information Retrieval, 2018, pp. 1033–1036.

W.-F. Chen, S. Syed, B. Stein, M. Hagen, and M. Potthast, “Abstractive Snippet Generation,” in Proceedings of the Web Conference 2020, 2020, pp. 1309–1319.

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.

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.

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.

M.-H. Chen, W.-F. Chen, and L.-W. Ku, “Application of Sentiment Analysis to Language Learning,” IEEE Access, vol. 6, pp. 24433–24442, 2018.

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.

M. Alshomary and M. Stahl, “Argument Novelty and Validity Assessment via Multitask and Transfer Learning,” in Proceedings of the 9th Workshop on Argument Mining, 2022, pp. 111–114.

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.

M. Alshomary, S. Syed, M. Potthast, and H. Wachsmuth, “Argument Undermining: Counter-Argument Generation by Attacking Weak Premises,”10.18653/v1/2021.findings-acl.159__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 .

H. Wachsmuth et al., “Argumentation Quality Assessment: Theory vs. Practice,” in Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 2017, pp. 250–255.

H. Wachsmuth, M. Stede, R. El Baff, K. Al Khatib, M. Skeppstedt, and B. Stein, “Argumentation Synthesis following Rhetorical Strategies,” in Proceedings of the 27th International Conference on Computational Linguistics, 2018, pp. 3753–3765.