Paper Info
Title
Neural Automated Essay Scoring and Coherence Modeling for Adversarially Crafted Input.
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.
Year
DOI
Venue
2018
10.18653/v1/N18-1024
north american chapter of the association for computational linguistics
DocType
Volume
ISSN
Journal
abs/1804.06898
The 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2018)
Citations 
PageRank 
References 
4
0.51
13
Authors
3
Name
Order
Citations
PageRank
Youmna Farag150.87
Helen Yannakoudakis2152.53
ted briscoe31560221.91