| Challenge: | Existing approaches to automatic essay scoring use low-level character n-gram features. |
| Approach: | They propose to combine string kernels and word embeddings for automatic essay scoring. |
| Outcome: | The proposed method outperforms state-of-the-art deep learning methods in Arabic dialect identification and native language identification tasks. |
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Enhancing Automated Essay Scoring Performance via Fine-tuning Pre-trained Language Models with Combination of Regression and Ranking (2020.findings-emnlp)
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| Challenge: | Recent work on sentence prediction tasks uses shallow neural networks to learn essay representations and constrain calculated scores with regression loss or ranking loss. |
| Approach: | They propose to use a pre-trained language model to learn text representations first and then to constrain the scores with regression loss or ranking loss. |
| Outcome: | The proposed model outperforms state-of-the-art models on the Automated Student Assessment Prize dataset. |
ASAP++: Enriching the ASAP Automated Essay Grading Dataset with Essay Attribute Scores (L18-1)
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| Challenge: | Automated essay grading (AEG) is one of the most challenging activities in natural language processing (NLP). |
| Approach: | They propose to annotate the ASAP AEG dataset and use it to score different attributes of the essays. |
| Outcome: | The proposed resource is based on the ASAP++ dataset, which contains scores for different attributes of the essays, such as content, word choice, organization, sentence fluency, etc. |
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 . |
Beyond the Gold Standard in Analytic Automated Essay Scoring (2025.acl-srw)
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| Challenge: | Automated Essay Scoring (AES) is a new approach to assessing writing practice . traditional holistic scoring methods are not reliable and lack formative feedback in the classroom. |
| Approach: | They propose to combine analytic and holistic AES to create a system that learns from individual raters instead of gold standard labels. |
| Outcome: | The proposed system learns from individual raters instead of gold standard labels. |
Automated Essay Scoring System for Nonnative Japanese Learners (2020.lrec-1)
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| Challenge: | Existing systems only provide a holistic score that summarizes the quality of an essay, which provides little feedback for a language learner. |
| Approach: | They developed an automated essay scoring system for Japanese as a second language learners using an essay dataset with annotations for a holistic score and multiple trait scores. |
| Outcome: | The proposed system achieves the highest accuracy in various natural language processing tasks. |
Neural Automated Essay Scoring Incorporating Handcrafted Features (2020.coling-main)
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| Challenge: | Automated essay scoring (AES) relies on handcrafted features, but recent studies have proposed a hybrid method that integrates handcrafted essay-level features into a DNN-AES model. |
| Approach: | They propose a method that integrates handcrafted features into a DNN-AES model. |
| Outcome: | The proposed method significantly improves the accuracy of existing methods. |
Multi-task Learning for Automated Essay Scoring with Sentiment Analysis (2020.aacl-srw)
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| Challenge: | Automated Essay Scoring (AES) is a process that aims to alleviate the workload of graders and improve the feedback cycle in educational systems. |
| Approach: | They propose to combine two tasks, sentiment analysis and AES by utilizing multi-task learning to combine sentiment features extracted from opinion expressions. |
| Outcome: | The proposed model produces a QWK of 0.763 on the Automated StudentAssessment Prize (ASAP) benchmark. |
Beyond Canonical Fine-tuning: Leveraging Hybrid Multi-Layer Pooled Representations of BERT for Automated Essay Scoring (2024.lrec-main)
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| Challenge: | Existing work on automated essay scoring focuses on capturing deep semantic features but are limited to lower-level textual features. |
| Approach: | They propose to use BERT's multi-layer architecture to leverage hierarchical linguistic information from its intermediate layers to improve overall essay scoring performance. |
| Outcome: | The proposed model outperforms the standard model with the default output on the ASAP AES dataset. |
T-MES: Trait-Aware Mix-of-Experts Representation Learning for Multi-trait Essay Scoring (2025.coling-main)
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| Challenge: | Existing methods for automatic essay scoring fail to learn trait representations and ignore correlations between trait scores. |
| Approach: | They propose a multi-trait essay scoring method based on Trait-Aware Mix-of-Experts Representation Learning. |
| Outcome: | The proposed method improves on existing methods and improves in computational efficiency. |
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. |