  We demonstrate that current state-of-the-art approaches to Automated Essay
Scoring (AES) are not well-suited to capturing adversarially crafted input of
grammatical but incoherent sequences of sentences. We develop a neural model of
local coherence that can effectively learn connectedness features between
sentences, and propose a framework for integrating and jointly training the
local coherence model with a state-of-the-art AES model. We evaluate our
approach against a number of baselines and experimentally demonstrate its
effectiveness on both the AES task and the task of flagging adversarial input,
further contributing to the development of an approach that strengthens the
validity of neural essay scoring models.
