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        <title>Operationalizing a National Digital Library: The Case for a Norwegian Transformer Model</title>
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        <namePart type="given">Per</namePart>
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    <abstract>In this work, we show the process of building a large-scale training set from digital and digitized collections at a national library. The resulting Bidirectional Encoder Representations from Transformers (BERT)-based language model for Norwegian outperforms multilingual BERT (mBERT) models in several token and sequence classification tasks for both Norwegian Bokmål and Norwegian Nynorsk. Our model also improves the mBERT performance for other languages present in the corpus such as English, Swedish, and Danish. For languages not included in the corpus, the weights degrade moderately while keeping strong multilingual properties. Therefore, we show that building high-quality models within a memory institution using somewhat noisy optical character recognition (OCR) content is feasible, and we hope to pave the way for other memory institutions to follow.</abstract>
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%0 Conference Proceedings
%T Operationalizing a National Digital Library: The Case for a Norwegian Transformer Model
%A Kummervold, Per E.
%A De la Rosa, Javier
%A Wetjen, Freddy
%A Brygfjeld, Svein Arne
%Y Dobnik, Simon
%Y Øvrelid, Lilja
%S Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa)
%D 2021
%8 may 31–2 jun
%I Linköping University Electronic Press, Sweden
%C Reykjavik, Iceland (Online)
%F kummervold-etal-2021-operationalizing
%X In this work, we show the process of building a large-scale training set from digital and digitized collections at a national library. The resulting Bidirectional Encoder Representations from Transformers (BERT)-based language model for Norwegian outperforms multilingual BERT (mBERT) models in several token and sequence classification tasks for both Norwegian Bokmål and Norwegian Nynorsk. Our model also improves the mBERT performance for other languages present in the corpus such as English, Swedish, and Danish. For languages not included in the corpus, the weights degrade moderately while keeping strong multilingual properties. Therefore, we show that building high-quality models within a memory institution using somewhat noisy optical character recognition (OCR) content is feasible, and we hope to pave the way for other memory institutions to follow.
%U https://aclanthology.org/2021.nodalida-main.3
%P 20-29

Markdown (Informal)

[Operationalizing a National Digital Library: The Case for a Norwegian Transformer Model](https://aclanthology.org/2021.nodalida-main.3) (Kummervold et al., NoDaLiDa 2021)

• Operationalizing a National Digital Library: The Case for a Norwegian Transformer Model (Kummervold et al., NoDaLiDa 2021)

ACL

• Per E Kummervold, Javier De la Rosa, Freddy Wetjen, and Svein Arne Brygfjeld. 2021. Operationalizing a National Digital Library: The Case for a Norwegian Transformer Model. In Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa), pages 20–29, Reykjavik, Iceland (Online). Linköping University Electronic Press, Sweden.