Part-of-Speech (POS) tags routinely appear as features in morphological tasks. POS taggers are often one of the first NLP tools developed for low-resource languages. However, as NLP expands to new languages it cannot assume that POS tags will be available to train a POS tagger. This paper empirically examines the impact of POS tags on two morphological tasks with the Transformer architecture. Each task is run twice, once with and once without POS tags, on otherwise identical data from ten

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... escribed languages and five underdocumented languages. We find that the presence or absence of POS tags does not have a significant bearing on the performance of either task. In joint segmentation and glossing, the largest average difference is an .09 improvement in F 1 -scores by removing POS tags. In reinflection, the greatest average difference is 1.2% in accuracy for published data and 5% for unpublished data. These results are indicators that NLP and documentary linguistics may benefit each other even when a POS tag set does not yet exist for a language.