Creating language technology based on language data has become very popular with the recent advances of large language models and neural network technologies. This makes language resources very valuable, and especially in case of indigenous languages, the scarce resources are even more precious. Given the good results of simply fetching everything you can from the internet and feeding it to neural networks in English, there has been more work on doing the same for all languages. However, indigenous language resources as they are on the web are not comparable in that they would encode the most recent normativised language in all its aspects. This problematic is further due to not understanding the texts input to models or output by models by the people who work on them. Corpora also have intelligent property rights and copyrights that are not respected. Furthermore, the web is filled with the result of language model -generated texts. In this article we describe an ethical and sustainable way to work with indigenous languages.

Indigenous language expertise is not encoded in written text in the same way as it is for languages that have a long literal tradition. In many cases it is, on the contrary, mostly conserved orally. Therefore the evaluation of neural MT systems solely based on an algorithm learning from written texts is not adequate to measure the quality of a system that is used by the language community. If extensively using tools based on a big amount of non-native language this can even contribute to language change in a way that is not desired by the language community. It can also pollute the internet with automatically created texts that outweigh native texts. We propose a manual evaluation method focusing on flow and content separately, and additionally we use existing rule-based NLP to evaluate other factors such as spelling, grammar and grammatical richness. Our main conclusion is that language expertise of a native speaker is necessary to properly evaluate a given system. We test the method by manually evaluating two neural MT tools for an indigenous low resource language. We present an experiment on two different neural translations to and from North Sámi, an indigenous language of North Europe.

We present GramDivvun, the first Inari Saami grammar checker for L2 users. The grammar checker is an important tool in the revitalisation of the language, in particular for strengthening the literary language. As the Inari Saami language community needs language tools predominantly for language learners, the focus is on grammatical interference errors made by (mostly Finnish-speaking) learners. Six of these errors are featured in the first version of the grammar checker. For non-proofread text written by inexperienced writers, precision is good, 73%. With experienced text and proofread text, alarms are rare but precision considerably lower, 19.5 % on average, but varying considerably between the error types. The paper discusses reasons for this variation. Future plans are improving results by means of increased testing, especially for complex sentences, and eventually also including more error types.

We present the first rule-based L1 grammar checker for Lule Sámi. Releasing a Lule Sámi grammar checker has direct consequences for language revitalization. Our primary intention is therefore to support language users in their writing and their confidence to use the language. We release a version of the tool for MS Word and GoogleDocs that corrects six grammatical error types. For the benefit of the user, the selection of error types is based on frequency of the errors and the quality of our tool. Our most successful error correction, for a phonetically and syntactically motivated copula error, reaches a precision of 96%.

We have released and evaluated the first South Sámi grammar checker GramDivvun. It corrects two frequent error types that are caused by and causing language change and a loss of the language’s morphological richness. These general error types comprise a number of errors regarding the adjective paradigm (confusion of attributive and predicative forms) and the negation paradigm. In addition, our work includes a classification of common error types regarding the adjective and negation paradigms and lead to extensive grammatical error mark-up of our gold corpus. We achieve precisions above 71% for both adjective and negation error correction.

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.

Grammar checkers (GEC) are needed for digital language survival. Very low resource languages like Lule Sámi with less than 3,000 speakers need to hurry to build these tools, but do not have the big corpus data that are required for the construction of machine learning tools. We present a rule-based tool and a workflow where the work done for a related language can speed up the process. We use an existing grammar to infer rules for the new language, and we do not need a large gold corpus of annotated grammar errors, but a smaller corpus of regression tests is built while developing the tool. We present a test case for Lule Sámi reusing resources from North Sámi, show how we achieve a categorisation of the most frequent errors, and present a preliminary evaluation of the system. We hope this serves as an inspiration for small languages that need advanced tools in a limited amount of time, but do not have big data.

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 rule-based 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.

We present a method for conducting morphological disambiguation for South Sámi, which is an endangered language. Our method uses an FST-based morphological analyzer to produce an ambiguous set of morphological readings for each word in a sentence. These readings are disambiguated with a Bi-RNN model trained on the related North Sámi UD Treebank and some synthetically generated South Sámi data. The disambiguation is done on the level of morphological tags ignoring word forms and lemmas; this makes it possible to use North Sámi training data for South Sámi without the need for a bilingual dictionary or aligned word embeddings. Our approach requires only minimal resources for South Sámi, which makes it usable and applicable in the contexts of any other endangered language as well.

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.