@inproceedings{haug-etal-2023-integrating,
title = "Rules and neural nets for morphological tagging of {N}orwegian - Results and challenges",
author = "Haug, Dag and
Yildirim, Ahmet and
Hagen, Kristin and
N{\o}klestad, Anders",
editor = {Alum{\"a}e, Tanel and
Fishel, Mark},
booktitle = "Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)",
month = may,
year = "2023",
address = "T{\'o}rshavn, Faroe Islands",
publisher = "University of Tartu Library",
url = "https://aclanthology.org/2023.nodalida-1.43",
pages = "425--435",
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.",
}
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%0 Conference Proceedings
%T Rules and neural nets for morphological tagging of Norwegian - Results and challenges
%A Haug, Dag
%A Yildirim, Ahmet
%A Hagen, Kristin
%A Nøklestad, Anders
%Y Alumäe, Tanel
%Y Fishel, Mark
%S Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)
%D 2023
%8 May
%I University of Tartu Library
%C Tórshavn, Faroe Islands
%F haug-etal-2023-integrating
%X 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.
%U https://aclanthology.org/2023.nodalida-1.43
%P 425-435
Markdown (Informal)
[Rules and neural nets for morphological tagging of Norwegian - Results and challenges](https://aclanthology.org/2023.nodalida-1.43) (Haug et al., NoDaLiDa 2023)
ACL