Challenge: Automated essay scoring (AES) is a task of assigning a single score to an essay . authors abandon sophisticated neural architectures and develop a simple feature-based approach .
Approach: a team of researchers develop a feature-based approach to cross-prompt automated essay scoring that adopts a simple neural architecture.
Outcome: a new approach to cross-prompt automated essay scoring can achieve state-of-the-art results.

Similar Papers

Cross-Prompt Automated Essay Scoring of Multiple Traits: Making Sense of the State of the Art (2026.acl-long)

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Challenge: despite recent progress in cross-prompt essay scoring, there is little analysis of what makes a state-of-the-art cross-propert scorer work well.
Approach: They propose to apply transductive learning to cross-prompt scoring for the first time . they propose to train a model that can offer good performance when applied to unseen prompts .
Outcome: The proposed model could be used in the rarely-studied classroom setting without additional training data.
Towards Prompt Generalization: Grammar-aware Cross-Prompt Automated Essay Scoring (2025.findings-naacl)

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Challenge: Existing approaches to score essays on unseen prompts are challenging to use in educational situations.
Approach: They propose a grammar-aware cross-prompt trait scoring model which internally captures prompt-independent syntactic aspects to learn generic essay representation.
Outcome: Empirical results show that the proposed model improves prompt-independent and grammar-related traits and achieves notable QWK gains in the most challenging cross-prompt scenario.
Automated Essay Scoring: A Reflection on the State of the Art (2024.emnlp-main)

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Challenge: Automated essay scoring (AES) is a key application of natural language processing . it is based on a holistic score that summarizes the essay's overall quality .
Approach: aaron carroll: automated essay scoring is one of the most important applications in NLP . carroll says the task is still far from being solved, but it's still progressing steadily . he says it'll be interesting to see how researchers can improve performance numbers .
Outcome: a new neural model can beat existing models on a standard evaluation dataset, authors say . authors: the current model is not enough to improve performance numbers . they say it could spark discussion among researchers on how to move forward .
PLAES: Prompt-generalized and Level-aware Learning Framework for Cross-prompt Automated Essay Scoring (2024.lrec-main)

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Challenge: Existing cross-prompt automatic essay scoring systems focus on obtaining shared knowledge specific to the target prompt, but this may not be feasible in practical situations because the target essay may not exist as training data.
Approach: They propose a novel learning framework for cross-prompt automatic essay scoring to capture more general knowledge across different prompts and improve the model’s capacity to distinguish between writing levels.
Outcome: The proposed learning framework captures more general knowledge across prompts and improves its capacity to distinguish between writing levels.
PMAES: Prompt-mapping Contrastive Learning for Cross-prompt Automated Essay Scoring (2023.acl-long)

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Challenge: Current cross-prompt automated essay scoring systems are limited by their ability to extract features directly from the original prompt.
Approach: They propose a method to learn more shared features between the source and target prompts by using a "prompt-mapping" approach to obtain more shared feature representations between the two prompts .
Outcome: The proposed method can be applied to a ASAP++ dataset showing that it is highly efficient and consistent.
TDNN: A Two-stage Deep Neural Network for Prompt-independent Automated Essay Scoring (P18-1)

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Challenge: Existing automated essay scoring (AES) models rely on rated essays for the target prompt as training data.
Approach: They propose a shallow deep neural network to learn a prompt-dependent rating model using rated essays for non-target prompts as training data.
Outcome: The proposed model improves on the standard ASAP dataset.
Improving Domain Generalization for Prompt-Aware Essay Scoring via Disentangled Representation Learning (2023.acl-long)

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Challenge: Existing AES models are either prompt-specific or prompt-adaptive and cannot generalize well on “unseen” prompts.
Approach: They propose a prompt-aware neural AES model to extract comprehensive representation for essay scoring, including both prompt-invariant and prompt-specific features.
Outcome: The proposed model extracts comprehensive representation for essay scoring, including both prompt-invariant and prompt-specific features.
Neural Automated Essay Scoring and Coherence Modeling for Adversarially Crafted Input (N18-1)

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Challenge: Existing approaches to Automated Essay Scoring (AES) are not well-suited to capture adversarially crafted input of grammatical but incoherent sequences of sentences.
Approach: They propose a neural model of local coherence that can effectively learn connectedness features between sentences.
Outcome: The proposed approach strengthens the validity of neural essay scoring models.
Prompt- and Trait Relation-aware Cross-prompt Essay Trait Scoring (2023.findings-acl)

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Challenge: Existing systems assume to grade essays on same prompt as used in training and assign only a holistic score.
Approach: They propose a prompt- and trait relation-aware cross-prompt essay trait scorer that encodes prompt-awful essay representation by essay-promotion attention and utilizing the topic-coherence feature extracted by the topic model.
Outcome: The proposed model shows state-of-the-art results for all prompts and traits.
MAPLE: A Meta-learning Framework for Cross-Prompt Essay Scoring (2026.findings-acl)

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Challenge: Current approaches to automate essay scoring (AES) treat each writing task as a separate task, resulting in inconsistent performance.
Approach: They propose a meta-learning framework that leverages prototypical networks to learn transferable representations across different writing prompts.
Outcome: The proposed framework outperforms baseline models on ELLIPSE and ASAP (English) and LAILA (Arabic) on three diverse datasets.

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