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        <title>Neural Automated Essay Scoring and Coherence Modeling for Adversarially Crafted Input</title>
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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.</abstract>
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%0 Conference Proceedings
%T Neural Automated Essay Scoring and Coherence Modeling for Adversarially Crafted Input
%A Farag, Youmna
%A Yannakoudakis, Helen
%A Briscoe, Ted
%Y Walker, Marilyn
%Y Ji, Heng
%Y Stent, Amanda
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F farag-etal-2018-neural
%X 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.
%R 10.18653/v1/N18-1024
%U https://aclanthology.org/N18-1024
%U https://doi.org/10.18653/v1/N18-1024
%P 263-271

Markdown (Informal)

[Neural Automated Essay Scoring and Coherence Modeling for Adversarially Crafted Input](https://aclanthology.org/N18-1024) (Farag et al., NAACL 2018)

• Neural Automated Essay Scoring and Coherence Modeling for Adversarially Crafted Input (Farag et al., NAACL 2018)

ACL

• Youmna Farag, Helen Yannakoudakis, and Ted Briscoe. 2018. Neural Automated Essay Scoring and Coherence Modeling for Adversarially Crafted Input. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 263–271, New Orleans, Louisiana. Association for Computational Linguistics.