Isolationist policy
Word analysis in isolating languages such as Vietnamese and Chinese is generally viewed as a problem of segmentation: given a sentence of tokens we aim to find the boundaries and segment the sequence into words.
From a morphology perspective, however, we might say that the text is over-segmented into a string of morphs, so that our task is to group morphs into chunks of words. The nice aspect of this view is that it falls nicely under what I would call sequential models of morphological analysis for non-isolating languages, where we have a lattice of possible segmentations (from some hypothesis generator) and we aim to find the best path. The isolating case just has a more constrained graph topology, with N+1 states for the N tokens and possible arcs limited by the maximum token length of words in the languages.
It’s also interesting that we run into the same type of problem here as with the analysis of non-isolating languages. Specifically, if the arc weights are something like probabilities, we will be biased toward longer words and thus fewer arcs, unless the costs of longer arcs are suitably balanced. With token classes and some labeled training data, we certainly might build highly accurate models. For a more unsupervised approach, however, seeking some intrinsic measure for an optimal segmentation, we run into the usual issue that there is no universally correct trade-off between coarse and fine analyses. We are always left with parameters, hidden or overt, that guide our models one way or another toward our preferences, which are, to a large degree, quite arbitrary.