Abstract
This paper reports on efforts to improve the Oslo-Bergen Tagger for Norwegian morphological tagging. We train two deep neural network-based taggers using the recently introduced Norwegian pre-trained encoder (a BERT model for Norwegian). The first network is a sequence-to-sequence encoder-decoder and the second is a sequence classifier. We test both these configurations in a hybrid system where they combine with the existing rule-based system, and on their own. The sequence-to-sequence system performs better in the hybrid configuration, but the classifier system performs so well that combining it with the rules is actually slightly detrimental to performance.- Anthology ID:
- 2023.nodalida-1.43
- Volume:
- Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)
- Month:
- May
- Year:
- 2023
- Address:
- Tórshavn, Faroe Islands
- Editors:
- Tanel Alumäe, Mark Fishel
- Venue:
- NoDaLiDa
- SIG:
- Publisher:
- University of Tartu Library
- Note:
- Pages:
- 425–435
- Language:
- URL:
- https://aclanthology.org/2023.nodalida-1.43
- DOI:
- Cite (ACL):
- Dag Haug, Ahmet Yildirim, Kristin Hagen, and Anders Nøklestad. 2023. Rules and neural nets for morphological tagging of Norwegian - Results and challenges. In Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa), pages 425–435, Tórshavn, Faroe Islands. University of Tartu Library.
- Cite (Informal):
- Rules and neural nets for morphological tagging of Norwegian - Results and challenges (Haug et al., NoDaLiDa 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2023.nodalida-1.43.pdf