We demonstrate that current state-of-theart 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.
REF demonstrated that state-of-the-art neural automated essay scoring (AES) is not well-suited for capturing adversarial input of grammatically correct but incoherent sequences of sentences.
Neural Automated Essay Scoring and Coherence Modeling for Adversarially Crafted Input
The 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2018)
Computer Science