Taxonomic Loss for Morphological Glossing of Low-Resource Languages
Michael Ginn Alexis Palmer
Morpheme glossing is a critical task in automated language documentation and
can benefit other downstream applications greatly. While state-of-the-art
glossing systems perform very well for languages with large amounts of existing
data, it is more difficult to create useful models for low-resource languages.
In this paper, we propose the use of a taxonomic loss function that exploits
morphological information to make morphological glossing more performant when
data is scarce. We find that while the use of this loss function does not
outperform a standard loss function with regards to single-label prediction
accuracy, it produces better predictions when considering the top-n predicted
labels. We suggest this property makes the taxonomic loss function useful in a
human-in-the-loop annotation setting.