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Neural Automated Essay Scoring and Coherence Modeling for Adversarially Crafted Input

Accepted version
Peer-reviewed

Type

Conference Object

Change log

Authors

Farag, Youmna 
Yannakoudakis, Helen  ORCID logo  https://orcid.org/0000-0002-4429-7729
Briscoe, Ted 

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.

Description

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,

Journal ISSN

Volume Title

Volume 1

Publisher

Association for Computational Linguistics
Sponsorship
Cambridge Assessment (unknown)
Engineering and Physical Sciences Research Council (1778176)