TAMS: Translation-Assisted Morphological Segmentation
Enora Rice Ali Marashian Luke Gessler Alexis Palmer Katharina von der Wense
Canonical morphological segmentation is the process of analyzing words into
the standard (aka underlying) forms of their constituent morphemes. This is a
core task in language documentation, and NLP systems have the potential to
dramatically speed up this process. But in typical language documentation
settings, training data for canonical morpheme segmentation is scarce, making
it difficult to train high quality models. However, translation data is often
much more abundant, and, in this work, we present a method that attempts to
leverage this data in the canonical segmentation task. We propose a
character-level sequence-to-sequence model that incorporates representations of
translations obtained from pretrained high-resource monolingual language models
as an additional signal. Our model outperforms the baseline in a super-low
resource setting but yields mixed results on training splits with more data.
While further work is needed to make translations useful in higher-resource
settings, our model shows promise in severely resource-constrained settings.