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We formulate coherence modeling as a regression task and propose two novelmethods to combine techniques from our setup with pairwise approaches. Thefirst of our methods is a model that we call "first-next," which operatessimilarly to selection sorting but conditions decision-making on informationabout already-sorted sentences. The second consists of a technique for addingcontext to regression-based models by concatenating sentence-levelrepresentations with an encoding of its corresponding out-of-order paragraph.This latter model achieves Kendall-tau distance and positional accuracy scoresthat match or exceed the current state-of-the-art on these metrics. Our resultssuggest that many of the gains that come from more complex, machine-translationinspired approaches can be achieved with simpler, more efficient models.

Context is Key: New Approaches to Neural Coherence Modeling