We investigate both rule-based and machine learning methods for the task of compound error correction and evaluate their efficiency for North Sámi, a low resource language. The lack of error-free data needed for a neural approach is a challenge to the development of these tools, which is not shared by bigger languages. In order to compensate for that, we used a rulebased grammar checker to remove erroneous sentences and insert compound errors by splitting correct compounds. We describe how we set up the error detection rules, and how we train a bi-RNN based neural network. The precision of the rule-based model tested on a corpus with real errors (81.0%) is slightly better than the neural model (79.4%). The rule-based model is also more flexible with regard to fixing specific errors requested by the user community. However, the neural model has a better recall (98%). The results suggest that an approach that combines the advantages of both models would be desirable in the future. Our tools and data sets are open-source and freely available on GitHub and Zenodo.

Currently, machine learning is presented as the ultimate solution for language technology regardless of use case and application, however, it requires as a starting point a massive amount of curated linguistic data in electronic form that is expected to be high quality and representative of the kind of language usage that the tools will follow. For minority and indigenous languages, this can be an insurmountable task, as digital materials of the necessary sizes do not exist and can not easily be produced. In this article we present an approach we have successfully used for supporting indigenous languages to survive and grow in digital contexts for years, and describe the potential of our approach for African contexts. Our technological solution is a free and open-source infrastructure that enables language experts and users to cooperate on creating linguistic resources like dictionaries and grammatical descriptions. In addition we provide language-independent frameworks to build these into applications that are needed by the language community.

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