While it is true that coherence is a property of a passage as a whole, capturing long-term dependencies in sequences remains a fundamental challenge when training neural networks in practice (Trinh et al., 2018).
it is plausible that much of global coherence can be decomposed into a series of local decisions, as demonstrated by foundational theories such as Centering Theory.