F. S. Bäumer, W.-F. Chen, M. Geierhos, J. Kersting, and H. Wachsmuth, “Dialogue-based Requirement Compensation and Style-adjusted Data-to-text Generation,” in On-The-Fly Computing -- Individualized IT-services in dynamic markets, vol. 412, C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, and H. Wehrheim, Eds. Paderborn: Heinz Nixdorf Institut, Universität Paderborn, 2023, pp. 65–84.

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.

M.-H. Chen, G. Mudgal, W.-F. Chen, and H. Wachsmuth, “Investigating the argumentation structures of EFL learners from diverse language backgrounds,” 2022.

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.

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.

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.

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.

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.

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, K. Al-Khatib, M. Hagen, H. Wachsmuth, and B. Stein, “Unraveling the Search Space of Abusive Language in Wikipedia with Dynamic Lexicon Acquisition,” in Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom, 2019, pp. 76–82.

W.-F. Chen and L.-W. Ku, “We Like, We Post: A Joint User-Post Approach for Facebook Post Stance Labeling,” IEEE Transactions on Knowledge and Data Engineering, vol. 30, no. 10, pp. 2013–2023, 2018.

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.

Chen W.-F. and Ku L.-W., “N2d`c5fŠ9efNf6 CSentiPackage c\55ac9,” ff8[78Œc7Š0ayd1[78, 2018.

W.-F. Chen, H. Wachsmuth, K. Al Khatib, and B. Stein, “Learning to Flip the Bias of News Headlines,” in Proceedings of the 11th International Conference on Natural Language Generation, 2018, pp. 79–88.

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.

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.

Y. Ajjour, W.-F. Chen, J. Kiesel, H. Wachsmuth, and B. Stein, “Unit Segmentation of Argumentative Texts,” in Proceedings of the 4th Workshop on Argument Mining, 2017, pp. 118–128.

W.-F. Chen, Y.-P. Chen, and L.-W. Ku, “How to Get Endorsements? Predicting Facebook Likes Using Post Content and User Engagement,” in International Conference on HCI in Business, Government, and Organizations, 2017, pp. 190–202.

W.-F. Chen and L.-W. Ku, “UTCNN: a Deep Learning Model of Stance Classification on Social Media Text,” in Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics, 2016, pp. 1635–1645.

W.-F. Chen, F.-Y. Lin, and L.-W. Ku, “WordForce: Visualizing Controversial Words in Debates,” in Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations, 2016, pp. 273–277.