  We present a novel and effective technique for performing text coherence
tasks while facilitating deeper insights into the data. Despite obtaining
ever-increasing task performance, modern deep-learning approaches to NLP tasks
often only provide users with the final network decision and no additional
understanding of the data. In this work, we show that a new type of sentence
embedding learned through self-supervision can be applied effectively to text
coherence tasks while serving as a window through which deeper understanding of
the data can be obtained. To produce these sentence embeddings, we train a
recurrent neural network to take individual sentences and predict their
location in a document in the form of a distribution over locations. We
demonstrate that these embeddings, combined with simple visual heuristics, can
be used to achieve performance competitive with state-of-the-art on multiple
text coherence tasks, outperforming more complex and specialized approaches.
Additionally, we demonstrate that these embeddings can provide insights useful
to writers for improving writing quality and informing document structuring,
and assisting readers in summarizing and locating information.
