@inproceedings{dannells-etal-2024-transformer,
    title = "Transformer-based {S}wedish Semantic Role Labeling through Transfer Learning",
    author = "Dann{\'e}lls, Dana  and
      Johansson, Richard  and
      Yang Buhr, Lucy",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.1458",
    pages = "16762--16769",
    abstract = "Semantic Role Labeling (SRL) is a task in natural language understanding where the goal is to extract semantic roles for a given sentence. English SRL has achieved state-of-the-art performance using Transformer techniques and supervised learning. However, this technique is not a viable choice for smaller languages like Swedish due to the limited amount of training data. In this paper, we present the first effort in building a Transformer-based SRL system for Swedish by exploring multilingual and cross-lingual transfer learning methods and leveraging the Swedish FrameNet resource. We demonstrate that multilingual transfer learning outperforms two different cross-lingual transfer models. We also found some differences between frames in FrameNet that can either hinder or enhance the model{'}s performance. The resulting end-to-end model is freely available and will be made accessible through Spr{\aa}kbanken Text{'}s research infrastructure.",
}

Semantic Role Labeling (SRL) is a task in natural language understanding where the goal is to extract semantic roles for a given sentence. English SRL has achieved state-of-the-art performance using Transformer techniques and supervised learning. However, this technique is not a viable choice for smaller languages like Swedish due to the limited amount of training data. In this paper, we present the first effort in building a Transformer-based SRL system for Swedish by exploring multilingual and cross-lingual transfer learning methods and leveraging the Swedish FrameNet resource. We demonstrate that multilingual transfer learning outperforms two different cross-lingual transfer models. We also found some differences between frames in FrameNet that can either hinder or enhance the model{'}s performance. The resulting end-to-end model is freely available and will be made accessible through Spr{\aa}kbanken Text{'}s research infrastructure.
Dann{\'e}lls, Dana Johansson, Richard Yang Buhr, Lucy
Transformer-based Swedish Semantic Role Labeling through Transfer Learning