Neural Automated Essay Scoring and Coherence Modeling for Adversarially Crafted Input
Accepted version
Peer-reviewed
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Abstract
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.
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Keywords
cs.CL, cs.CL, cs.AI
Journal Title
Proceedings of NAACL-HLT 2018, New Orleans, Louisiana, pages 263–271
Conference Name
NAACL-HLT 2018,
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Volume Title
Volume 1
Publisher
Association for Computational Linguistics
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Sponsorship
Cambridge Assessment (unknown)
Engineering and Physical Sciences Research Council (1778176)
Engineering and Physical Sciences Research Council (1778176)