  Data scarcity is a widespread problem in numerous natural language processing
(NLP) tasks for low-resource languages. Within morphology, the labour-intensive
work of tagging/glossing data is a serious bottleneck for both NLP and language
documentation. Active learning (AL) aims to reduce the cost of data annotation
by selecting data that is most informative for improving the model. In this
paper, we explore four sampling strategies for the task of morphological
inflection using a Transformer model: a pair of oracle experiments where data
is chosen based on whether the model already can or cannot inflect the test
forms correctly, as well as strategies based on high/low model confidence,
entropy, as well as random selection. We investigate the robustness of each
strategy across 30 typologically diverse languages. We also perform a more
in-depth case study of Nat\"ugu. Our results show a clear benefit to selecting
data based on model confidence and entropy. Unsurprisingly, the oracle
experiment, where only incorrectly handled forms are chosen for further
training, which is presented as a proxy for linguist/language consultant
feedback, shows the most improvement. This is followed closely by choosing
low-confidence and high-entropy predictions. We also show that despite the
conventional wisdom of larger data sets yielding better accuracy, introducing
more instances of high-confidence or low-entropy forms, or forms that the model
can already inflect correctly, can reduce model performance.
