  Recent advancements in morpheme segmentation primarily emphasize word-level
segmentation, often neglecting the contextual relevance within the sentence. In
this study, we redefine the morpheme segmentation task as a
sequence-to-sequence problem, treating the entire sentence as input rather than
isolating individual words. Our findings reveal that the multilingual model
consistently exhibits superior performance compared to monolingual
counterparts. While our model did not surpass the performance of the current
state-of-the-art, it demonstrated comparable efficacy with high-resource
languages while revealing limitations in low-resource language scenarios.
