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The paper introduces a rule-based computational model for Gaidhlig morphology, addressing the challenge of limited data availability for low-resource languages that hinders the application of neural models. The model leverages data from Wiktionary and uses SQL queries to identify lexical patterns, constructing a declarative rule-base for generating inflected word forms via Python utilities. This approach demonstrates that rule-based systems can effectively utilize limited data while providing interpretability and supporting the development of educational tools.
Rule-based models offer a practical and interpretable alternative to data-hungry neural networks for morphologically rich, low-resource languages like Scottish Gaelic.
Language models and software tools are essential to support the continuing vitality of lesser-used languages; however, currently popular neural models require considerable data for training, which normally is not available for such low-resource languages. This paper describes work-in-progress to construct a rule-based model of Gaidhlig morphology using data from Wiktionary, arguing that rule-based systems effectively leverage limited sample data, support greater interpretability, and provide insights useful in the design of teaching materials. The use of SQL for querying the occurrence of different lexical patterns is investigated, and a declarative rule-base is presented that allows Python utilities to derive inflected forms of Gaidhlig words. This functionality could be used to support educational tools that teach or explain language patterns, for example, or to support higher level tools such as rule-based dependency parsers. This approach adds value to the data already present in Wiktionary by adapting it to new use-cases.