A4 Refereed article in a conference publication
The FISKMO project: Resources and tools for Finnish-Swedish machine translation and cross-linguistic research
Authors: Jörg Tiedemann, Tommi Nieminen, Mikko Aulamo, Jenna Kanerva, Akseli Leino, Filip Ginter, Niko Papula
Editors: Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Conference name: International Conference on Language Resources and Evaluation
Publisher: European Language Resources Association (ELRA)
Publishing place: Marseille, France
Publication year: 2020
Book title : Proceedings of the 12th Language Resources and Evaluation Conference
Journal name in source: LREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings
First page : 3808
Last page: 3815
ISBN: 978-10-95546-34-5
Web address : https://www.aclweb.org/anthology/2020.lrec-1.470/
Self-archived copy’s web address: http://research.utu.fi/converis/portal/Publication/51180793
This paper presents FISKMÖ, a project that focuses on the development of resources and tools for cross-linguistic research and machine translation between Finnish and Swedish. The goal of the project is the compilation of a massive parallel corpus out of translated material collected from web sources, public and private organisations and language service providers in Finland with its two official languages. The project also aims at the development of open and freely accessible translation services for those two languages for the general purpose and for domain-specific use. We have released new data sets with over 3 million translation units, a benchmark test set for MT development, pre-trained neural MT models with high coverage and competitive performance and a self-contained MT plugin for a popular CAT tool. The latter enables offline translation without dependencies on external services making it possible to work with highly sensitive data without compromising security concerns.
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