  Morphological Segmentation involves decomposing words into morphemes, the
smallest meaning-bearing units of language. This is an important NLP task for
morphologically-rich agglutinative languages such as the Southern African Nguni
language group. In this paper, we investigate supervised and unsupervised
models for two variants of morphological segmentation: canonical and surface
segmentation. We train sequence-to-sequence models for canonical segmentation,
where the underlying morphemes may not be equal to the surface form of the
word, and Conditional Random Fields (CRF) for surface segmentation.
Transformers outperform LSTMs with attention on canonical segmentation,
obtaining an average F1 score of 72.5% across 4 languages. Feature-based CRFs
outperform bidirectional LSTM-CRFs to obtain an average of 97.1% F1 on surface
segmentation. In the unsupervised setting, an entropy-based approach using a
character-level LSTM language model fails to outperforms a Morfessor baseline,
while on some of the languages neither approach performs much better than a
random baseline. We hope that the high performance of the supervised
segmentation models will help to facilitate the development of better NLP tools
for Nguni languages.
