mlsanityData to 14 Oct 2026

Neural Automated Essay Scoring and Coherence Modeling for Adversarially Crafted Input

arXiv 1804.06898 ↗First posted 18 Apr 2018cs.CL cs.AIcited by 16

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Abstract (arXiv)

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.

Key facts. Cited by 16 papers in the map (data through 14 Oct 2026). First posted on arXiv 18 Apr 2018.

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Citing papers per year: 2021: 4; 2022: 5; 2023: 1; 2024: 2; 2025: 3; 2026: 1.

16 papers.

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