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Data scarcity is a widespread problem in numerous natural language processing(NLP) tasks for low-resource languages. Within morphology, the labour-intensivework of tagging/glossing data is a serious bottleneck for both NLP and languagedocumentation. Active learning (AL) aims to reduce the cost of data annotationby selecting data that is most informative for improving the model. In thispaper, we explore four sampling strategies for the task of morphologicalinflection using a Transformer model: a pair of oracle experiments where datais chosen based on whether the model already can or cannot inflect the testforms correctly, as well as strategies based on high/low model confidence,entropy, as well as random selection. We investigate the robustness of eachstrategy across 30 typologically diverse languages. We also perform a morein-depth case study of Nat\"ugu. Our results show a clear benefit to selectingdata based on model confidence and entropy. Unsurprisingly, the oracleexperiment, where only incorrectly handled forms are chosen for furthertraining, which is presented as a proxy for linguist/language consultantfeedback, shows the most improvement. This is followed closely by choosinglow-confidence and high-entropy predictions. We also show that despite theconventional wisdom of larger data sets yielding better accuracy, introducingmore instances of high-confidence or low-entropy forms, or forms that the modelcan already inflect correctly, can reduce model performance.

Eeny, meeny, miny, moe. How to choose data for morphological inflection