Neural and rule-based Finnish NLP models—expectations, experiments and experiences
Tommi A. Pirinen · 2019
In this article I take a critical look at some recent results in the field of neural language modeling of Finnish in terms of popular shared tasks.One novel point of view I present is comparing the neural methods' results to traditional rule-based systems for the given tasks, since most of the shared tasks have concentrated on the supervised learning concept.The shared task results I re-evaluate, are morphological regeneration by SIGMORPHON 2016, universal dependency parsing by CONLL-2018 and a machine translation application that imitates WMT 2018 for German instead of English.The Uralic language used throughout is Finnish.I use out of the box, best performing neural systems and rule-based systems and evaluate their results. TiivistelmäTässä artikkelissa tarkastelemme joitain hiljattaisia tuloksia niinkutsutuissa shared task -kilpailuissa suomen kielen hemroverkkomallien osalta.Yksi tämän artikkelin kontribuutioista on hermoverkkomallien tuottamien tulosten vertailu perinteisiin sääntöpohjaisiin kielimallituloksiin, sillä shared task -kisailut pääosin keskittyvät täysin tai osittain hallitsemattomien mallien oppimisen konseptiin.