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 Språkbanken Text’s research infrastructure.- Anthology ID:
- 2024.lrec-main.1458
- Volume:
- Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
- Month:
- May
- Year:
- 2024
- Address:
- Torino, Italia
- Editors:
- Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
- Venues:
- LREC | COLING
- SIG:
- Publisher:
- ELRA and ICCL
- Note:
- Pages:
- 16762–16769
- Language:
- URL:
- https://aclanthology.org/2024.lrec-main.1458
- DOI:
- Cite (ACL):
- Dana Dannélls, Richard Johansson, and Lucy Yang Buhr. 2024. Transformer-based Swedish Semantic Role Labeling through Transfer Learning. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 16762–16769, Torino, Italia. ELRA and ICCL.
- Cite (Informal):
- Transformer-based Swedish Semantic Role Labeling through Transfer Learning (Dannélls et al., LREC-COLING 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.lrec-main.1458.pdf