Rules Ruling Neural Networks - Neural vs. Rule-Based Grammar Checking for a Low Resource Language

Proceedings of Recent Advances in Natural Language Processing, pages 1526–1535 Sep 1–3, 2021. https://doi.org/10.26615/978-954-452-072-4_171 1526 Rules Ruling Neural Networks – Neural vs. Rule-Based Grammar Checking for a Low Resource Language Linda Wiechetek UiT Norgga árktalaš universitehta Norway Flammie A Pirinen UiT Norgga árktalaš universitehta Norway Mika Hämäläinen University of Helsinki, Rootroo Ltd Finland Chiara Argese UiT Norgga árktalaš universitehta Norway Abstract We investigate both rule-based and machine learning methods for the task of compound er- ror 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 split- ting correct compounds. We describe how we set up the error detection rules, and how we train a bi-RNN based neural network. The pre- cision of the rule-based model tested on a cor- pus 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 fix- ing specific errors requested by the user com- munity. However, the neural model has a bet- ter 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. 1 Introduction This paper presents our work on automatically cor- recting compound errors in real world text of North Sámi and exploring both rule-based and neural net- work methods. We chose this error type as it is the most frequent grammatical error type (after spelling and punctuation errors) and twice as fre- quent as the second most frequent grammatical er- ror (agreement error). It also regards both spelling and grammar as the error is a space between two words, but its correction requires grammatical con- text. A grammar checker is a writer’s tool and partic- ularly relevant to improve writing skills of a minor- ity language in a bilingual context, as is the case for North Sámi. According to UNESCO (Mose- ley, 2010), North Sámi, spoken in the North of Norway, Sweden and Finland, has around 30,000 speakers. It is a low resource language in a bilin- gual setting, and language users frequently face bigger challenges to writing proficiency as there is always a competing language. (Outakoski, 2013) Developing a reliable grammar checker with a high precision that at the same time covers a lot of errors has therefore been our main focus. Good precision (i.e. avoiding false alarms) is a priority because users get easily frustrated if a grammar checker gives false alarms and underlines correct sentences. In this paper we focus on the correction of com- pound errors. This type of errors is easy to generate artificially in the absence of large amounts of error marked-up text, and we have a good …

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