Machine learning (ML) approaches have dominated NLP during the last two decades. From machine translation and speech technology, ML tools are now also in use for spellchecking and grammar checking, with a blurry distinction between the two. We unmask the myth of effortless big data by illuminating the efforts and time that lay behind building a multi-purpose corpus with regard to collecting, mark-up and building from scratch. We also discuss what kind of language technology minority languages actually need, and to what extent the dominating paradigm has been able to deliver these tools. In this context we present our alternative to corpus-based language technology, which is knowledge-based language technology, and we show how this approach can provide language technology solutions for languages being outside the reach of machine learning procedures. We present a stable and mature infrastructure (GiellaLT) containing more than hundred languages and building a number of language technology tools that are useful for language communities.

Grammatical approaches to language technology are often considered less optimal than statistical approaches in multilingual settings, where large-scale portability becomes an important issue. The present paper argues that there is a notable gain in reusing grammatical resources when porting technology to new languages. The pivot language is North Sámi, and the paper discusses portability with respect to the closely related Lule and South Sámi, and to the unrelated Faroese and Greenlandic languages.

We describe the development of a two-way shallow-transfer machine translation system between Norwegian Nynorsk and Norwegian Bokma ̊l built on the Apertium platform, using the Free and Open Source resources Norsk Ordbank and the Oslo–Bergen Constraint Grammar tagger. We detail the integration of these and other resources in the system along with the construction of the lexical and structural transfer, and evaluate the translation quality in comparison with another system. Finally, some future work is suggested.