Maja Lisa Kappfjell


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The Ethical Question – Use of Indigenous Corpora for Large Language Models
Linda Wiechetek | Flammie A. Pirinen | Børre Gaup | Trond Trosterud | Maja Lisa Kappfjell | Sjur Moshagen
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

Creating language technology based on language data has become very popular with the recent advances of large language models and neural network technologies. This makes language resources very valuable, and especially in case of indigenous languages, the scarce resources are even more precious. Given the good results of simply fetching everything you can from the internet and feeding it to neural networks in English, there has been more work on doing the same for all languages. However, indigenous language resources as they are on the web are not comparable in that they would encode the most recent normativised language in all its aspects. This problematic is further due to not understanding the texts input to models or output by models by the people who work on them. Corpora also have intelligent property rights and copyrights that are not respected. Furthermore, the web is filled with the result of language model -generated texts. In this article we describe an ethical and sustainable way to work with indigenous languages.


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A South Sámi Grammar Checker For Stopping Language Change
Linda Wiechetek | Maja Lisa Kappfjell
Proceedings of the NoDaLiDa 2023 Workshop on Constraint Grammar - Methods, Tools and Applications

We have released and evaluated the first South Sámi grammar checker GramDivvun. It corrects two frequent error types that are caused by and causing language change and a loss of the language’s morphological richness. These general error types comprise a number of errors regarding the adjective paradigm (confusion of attributive and predicative forms) and the negation paradigm. In addition, our work includes a classification of common error types regarding the adjective and negation paradigms and lead to extensive grammatical error mark-up of our gold corpus. We achieve precisions above 71% for both adjective and negation error correction.