Helene Bøsei Olsen

• Olsen, Helene Bøsei; Velldal, Erik & Øvrelid, Lilja (2026). MUC-4 Revisited: Document-level Event Analysis beyond Span-based Arguments. In Piperidis,, Stelios; Bel, Núria; Heuvel, Henk van den; Ide, Nancy; Krek, Simon & Toral, Antonio (Ed.), Proceedings of the Fifteenth Language Resources and Evaluation Conference (LREC 2026). European Language Resources Association. ISSN 9782493814494. p. 7766–7780. doi: 10.63317/2kaxd8nu44bx. Full text in Research Archive Show summary

  Automatically predicting structured representations of events has long been a central goal in information extraction, yet most contemporary work remains limited to identifying contiguous text spans as event arguments. This span-centric formulation fails to capture higher-level aspects of real-world events, such as actor identities, temporal scope, and aggregated outcomes, that many event-centred applications depend on. While commonly treated as a standard extractive benchmark, MUC-4 originally combined span-based arguments with normalised, inferred, and categorical fields, reflecting a richer, application-driven design. In this paper, we revisit MUC-4 in its full original formulation, casting it as an abstractive event analysis task that connects traditional event extraction goals with modern generative and document-level paradigms. We provide the first systematic evaluation of fine-tuned generative models in this extended formulation on MUC-4, examining how post-training stages and model size affect performance across both span-based and higher-level, semantically grounded event information. An extensive error analysis highlights practical challenges and directions for future work.

• Simon, Étienne; Olsen, Helene Bøsei; Villar, Ramón Carreño; Mishra, Rahul; Arefyev, Nikolay & Yilmaz, Mert Can [Show all 8 contributors for this article] (2025). Abstractive Event Analysis of Armed Conflicts: Introducing the UCDP-AEC Dataset. In Wartena, Christian & Heid, Ulrich (Ed.), Proceedings of the 21st Conference on Natural Language Processing (KONVENS 2025): Workshops. HsH Applied Academics. ISSN 9783690180177. p. 104–119. doi: https:/aclanthology.org/2025.konvens-2.8.pdf. Full text in Research Archive Show summary

  This paper introduces a new dataset of document-level event annotations in the domain of armed conflict. By augmenting the event database from the Uppsala Conflict Data Program (UCDP) with source documents identified in public web archives, we create the UCDP Abstractive Event analysis Corpus (UCDP-AEC). While a large part of research on information extraction is focused on extracting text spans, realworld use cases often require inferring more high-level information that is not necessarily explicitly mentioned in texts. UCDP-AEC differs from traditional event extraction datasets in that the document-level annotations do not correspond to mere text spans of the input, but capture expert-interpreted and often implicit information. With more than 10 000 documents, UCDP-AEC is of comparable size to the largest human-annotated traditional event extraction datasets. We also report preliminary experimental results for various generative approaches, by fine-tuning both decoder models and existing event argument extraction models that require minimal adaptation to our abstractive formulation of the task

• Olsen, Helene Bøsei; Simon, Étienne; Velldal, Erik & Øvrelid, Lilja (2024). Socio-political Events of Conflict and Unrest: A Survey of Available Datasets. In Hürriyetoğlu, Ali; Tanev, Hristo; Surendrabikram, Thapa & Uludoğan, Gökçe (Ed.), Proceedings of the 7th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2024). Association for Computational Linguistics (ACL). ISSN 9798891760707. p. 40–53. Full text in Research Archive
• Simon, Étienne; Olsen, Helene Bøsei; You, Huiling; Touileb, Samia; Øvrelid, Lilja & Velldal, Erik (2024). Generative Approaches to Event Extraction: Survey and Outlook. In Tetreault, Joel; Nguyen, Thien Huu; Lamba, Hemank & Hughes, Amanda (Ed.), Proceedings of the Workshop on the Future of Event Detection (FuturED). Association for Computational Linguistics (ACL). ISSN 9798891761735. p. 73–86. doi: 10.18653/v1/2024.futured-1.7. Full text in Research Archive
• Olsen, Helene Bøsei; Touileb, Samia & Velldal, Erik (2023). Arabic dialect identification: An in-depth error analysis on the MADAR parallel corpus. In Sawaf, Hassan; El-Beltagy, Samhaa; Zaghouani, Wajdi; Magdy, Walid; Abdelali, Ahmed; Tomeh, Nadi; Farha, Ibrahim Abu; Habash, Nizar; Khalifa, Salam; Keleg, Amr; Haddad, Hatem; Zitouni, Imed; Mrini, Khalil & Almatham, Rawan (Ed.), Proceedings of ArabicNLP 2023. Association for Computational Linguistics (ACL). ISSN 9781959429272. p. 370–384. doi: 10.18653/v1/2023.arabicnlp-1.30. Full text in Research Archive Show summary

  This paper provides a systematic analysis and comparison of the performance of state-of-the-art models on the task of fine-grained Arabic dialect identification using the MADAR parallel corpus. We test approaches based on pre-trained transformer language models in addition to Naive Bayes models with a rich set of various features. Through a comprehensive data- and error analysis, we provide valuable insights into the strengths and weaknesses of both approaches. We discuss which dialects are more challenging to differentiate, and identify potential sources of errors. Our analysis reveals an important problem with identical sentences across dialect classes in the test set of the MADAR-26 corpus, which may confuse any classifier. We also show that none of the tested approaches captures the subtle distinctions between closely related dialects.

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