Flammie Pirinen


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Unmasking the Myth of Effortless Big Data - Making an Open Source Multi-lingual Infrastructure and Building Language Resources from Scratch
Linda Wiechetek | Katri Hiovain-Asikainen | Inga Lill Sigga Mikkelsen | Sjur Moshagen | Flammie Pirinen | Trond Trosterud | Børre Gaup
Proceedings of the Thirteenth Language Resources and Evaluation Conference

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.

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Building an Extremely Low Resource Language to High Resource Language Machine Translation System from Scratch
Flammie Pirinen | Linda Wiechetek
Proceedings of the 18th Conference on Natural Language Processing (KONVENS 2022)


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Rules Ruling Neural Networks - Neural vs. Rule-Based Grammar Checking for a Low Resource Language
Linda Wiechetek | Flammie Pirinen | Mika Hämäläinen | Chiara Argese
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)

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.