Is it better to have one model with more categories or less with two for multi-label classification?
Generally there are two main options for multi-label classifications:

A binary model for every class, in this case the labels are supposed to be independent of each other.
A joint model which learns all the classes together, so at training stage the class represents the three values for the labels, for instance:

class &quot;NNN&quot; means no label at all
class &quot;NNY&quot; means no for question, no for complain and yes for complements
class &quot;NYN&quot; means no for question, yes for complain and no for complements
...
class &quot;YYY&quot; means yes for question, yes for complain and yes for complements



In theory there are $2^3$ possible classes for 3 labels, but in practice there might be less combinations in the data (as suggested in the question). If the labels depend on each other (for instance if it's unlikely to have a document which is both complain and complements), a joint model is more appropriate. However a joint model might need more instances to be trained properly, since it has more work to do than a binary model.