Abstract
Coherence is an important aspect of text quality and is crucial for ensuring its readability. One important limitation of existing coherence models is that training on one domain does not easily generalize to unseen categories of text. Previous work (Li and Jurafsky, 2017) advocates for generative models
for cross-domain generalization, because for
discriminative models, the space of incoherent sentence orderings to discriminate against
during training is prohibitively large. In this
work, we propose a local discriminative neural model with a much smaller negative sampling space that can efficiently learn against
incorrect orderings. The proposed coherence
model is simple in structure, yet it significantly
outperforms previous state-of-art methods on a
standard benchmark dataset on the Wall Street
Journal corpus, as well as in multiple new
challenging settings of transfer to unseen categories of discourse on Wikipedia articles.