Andre Kåsen


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Comparing Methods for Measuring Dialect Similarity in Norwegian
Janne Johannessen | Andre Kåsen | Kristin Hagen | Anders Nøklestad | Joel Priestley
Proceedings of the 12th Language Resources and Evaluation Conference

The present article presents four experiments with two different methods for measuring dialect similarity in Norwegian: the Levenshtein method and the neural long short term memory (LSTM) autoencoder network, a machine learning algorithm. The visual output in the form of dialect maps is then compared with canonical maps found in the dialect literature. All of this enables us to say that one does not need fine-grained transcriptions of speech to replicate classical classification patterns.

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Progress of the PRINCIPLE Project: Promoting MT for Croatian, Icelandic, Irish and Norwegian
Andy Way | Petra Bago | Jane Dunne | Federico Gaspari | Andre Kåsen | Gauti Kristmannsson | Helen McHugh | Jon Arild Olsen | Dana Davis Sheridan | Páraic Sheridan | John Tinsley
Proceedings of the 22nd Annual Conference of the European Association for Machine Translation

This paper updates the progress made on the PRINCIPLE project, a 2-year action funded by the European Commission under the Connecting Europe Facility (CEF) programme. PRINCIPLE focuses on collecting high-quality language resources for Croatian, Icelandic, Irish and Norwegian, which have been identified as low-resource languages, especially for building effective machine translation (MT) systems. We report initial achievements of the project and ongoing activities aimed at promoting the uptake of neural MT for the low-resource languages of the project.


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Tagging a Norwegian Dialect Corpus
Andre Kåsen | Anders Nøklestad | Kristin Hagen | Joel Priestley
Proceedings of the 22nd Nordic Conference on Computational Linguistics

This paper describes an evaluation of five data-driven part-of-speech (PoS) taggers for spoken Norwegian. The taggers all rely on different machine learning mechanisms: decision trees, hidden Markov models (HMMs), conditional random fields (CRFs), long-short term memory networks (LSTMs), and convolutional neural networks (CNNs). We go into some of the challenges posed by the task of tagging spoken, as opposed to written, language, and in particular a wide range of dialects as is found in the recordings of the LIA (Language Infrastructure made Accessible) project. The results show that the taggers based on either conditional random fields or neural networks perform much better than the rest, with the LSTM tagger getting the highest score.


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The LIA Treebank of Spoken Norwegian Dialects
Lilja Øvrelid | Andre Kåsen | Kristin Hagen | Anders Nøklestad | Per Erik Solberg | Janne Bondi Johannessen
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)