Dr. Sean Papay
Dr. Sean Papay ist Postdoc in der NLP-Gruppe der Universität Bamberg unter der Leitung von Professor Roman Klinger. Seine Forschungsinteressen konzentrieren sich auf maschinelles Lernen mit strukturierten Ausgaben und Techniken zur Integration von apriorischem Wissen über Ausgabe-Strukturen in Modelle. Zu seinen spezifischen Forschungsrichtungen gehören allgemeine Aufgaben der Relationsextraktion und eingeschränktes Sampling von generativen Modellen.
Bevor er an die Universität Bamberg kam, promovierte Dr.Papay als Doktorand an der Universität Stuttgart bei Professor Sebastian Padó.
Publikationen
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.