Dr. Sean Papay
Dr. Sean Papay is a Post-doc working under Professor Roman Klinger in the NLP group at the University of Bamberg.
His research interests center around machine learning with structured outputs, and techniques for incorporating a priori knowledge of output structures into models.Specific directions include research into task-general relation extraction, and constrained sampling from generative models.
Before arriving at the University of Bamberg, Dr. Papay studied as a doctoral candidate at the University of Stuttgart under Professor Sebastian Padó.
Publications
Rauf, Moiz/Papay, Sean (2026): Medical Summarization in Practice: Design, Deployment, and Analysis of a Clinical Summarization System for a German Hospital. In: Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics. Association for Computational Linguistics. S. 455–466.
Li, Jiahui/Papay, Sean/Klinger, Roman (2025): Are Humans as Brittle as Large Language Models?. In: Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics. Mumbai, India: The Asian Federation of Natural Language Processing and The Association for Computational Linguistics. S. 2130–2155.
Nikolaev, Dmitry/Papay, Sean (2025): Strategies for political-statement segmentation and labelling in unstructured text. In: Proceedings of the 5th International Conference on Natural Language Processing for Digital Humanities. Stroudsburg, PA: Association for Computational Linguistics. S. 437–451.
Papay, Sean/Klinger, Roman/Padó, Sebastian (2025): Regular-pattern-sensitive CRFs for Distant Label Interactions. In: Proceedings of the 1st Joint Workshop on Large Language Models and Structure Modeling (XLLM 2025). Association for Computational Linguistics. S. 26–35.
Schäfer, Johannes et al. (2025): Which Demographics do LLMs Default to During Annotation?. In: Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics. S. 17331–17348.
Barić, Ana/Padó, Sebastian/Papay, Sean (2024): Actor Identification in Discourse: A Challenge for LLMs?. In: Proceedings of the 5th Workshop on Computational Approaches to Discourse (CODI 2024). Association for Computational Linguistics. S. 64–70.
Ceron, Tanise et al. (2024): Automatic Analysis of Political Debates and Manifestos: Successes and Challenges. In: Robust argumentation machines: first international conference, RATIO 2024, Bielefeld, Germany, June 5-7, 2024: proceedings. Cham, Switzerland: Springer Nature. S. 71–88.
Chen, Jun/Papay, Sean (2024): Sentence‑Final Particle de in Mandarin as an Informativity Maximizer. In: Selected Reflections in Language, Logic, and Information: ESSLLI 2019, ESSLLI 2020 and ESSLLI 2021 Student Sessions, Selected Papers. Cham: Springer Nature Switzerland. S. 24–43.
Papay, Sean (2024): Task generality in relation extraction. Stuttgart: Universitätsbibliothek der Universität Stuttgart.
Papay, Sean/Klinger, Roman/Padó, Sebastian (2022): Constraining Linear-chain CRFs to Regular Languages. arxiv.
Papay, Sean/Klinger, Roman/Padó, Sebastian (2020): Dissecting Span Identification Tasks with Performance Prediction. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics. S. 4881–4895.
Adel, Heike et al. (2018): DERE: A Task and Domain-Independent Slot Filling Framework for Declarative Relation Extraction. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. Association for Computational Linguistics. S. 42–47.