Dr. Sean Papay
Dr. Sean Papay ist Postdoc in der NLP-Gruppe der Universit?t 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 geh?ren allgemeine Aufgaben der Relationsextraktion und eingeschr?nktes Sampling von generativen Modellen.
Bevor er an die Universit?t Bamberg kam, promovierte Dr.Papay als Doktorand an der Universit?t Stuttgart bei Professor Sebastian Padó.
Publikationen
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. Albuquerque, USA: 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.
Sch?fer, 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.
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.