 Current compositional semantics is beginning to combine compositional semantic theory (logic-based approaches discussed above) with statistical models, conforming to the standard view of compositionality on which complex meanings are a function of lexical meanings and the mode of combination, arrived at through a recursive process, but at the same time aiming at capturing the generalization from (finite) past experiences that would inform machine learning. Defaults arise in this context in several different forms: (i) as shortcuts to standard meanings of more semantically predictable categories, that is, closed-class words such as determiners, pronouns or sentential connectives. (This can be extended perhaps to types of predictable projective content such as various types of implicature or presupposition; see Tonhauser et al. 2013); (ii) as predictable cross-sentential discourse relations; (iii) as predictable discourse-anaphoric links; (iv) as meaning arising from frequent syntagmatic associations; (v) as meaning arising from frequent conversational scenarios, to name a few salient concepts. In this new, positively eclectic orientation in computational linguistics that combines logical and statistical approaches, the label ‘default’ is likely to lead to more confusion than utility in that it can pertain to either of the two contributing orientations. On the other hand, if the findings lead to the same set of what we can call ‘shortcuts through possible interpretations’, the confusion may be of merely a methodological rather than ontological importance.