Papers with automated essay scoring
LinggleWrite: a Coaching System for Essay Writing (2020.acl-demos)
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| Challenge: | Existing writing services that provide feedback on writing skills are not providing sufficient "coaching" information. |
| Approach: | They propose a writing coach that provides writing suggestions, assesses writing proficiency levels, detects grammatical errors, and offers corrective feedback in response to user’s essay. |
| Outcome: | The proposed system improves on public test sets and shows that both AES and GED models achieve state-of-the-art performance. |
Automated Essay Scoring in the Presence of Biased Ratings (N18-1)
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| Challenge: | Existing studies on rater effects in general settings have not investigated how rater bias affects automated essay scoring. |
| Approach: | They propose to model rater bias by removing essays associated with potentially biased scores from annotated corpus. |
| Outcome: | The proposed model is based on comments provided by raters and is compared with existing corpus. |
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. |
Autoregressive Score Generation for Multi-trait Essay Scoring (2024.findings-eacl)
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| Challenge: | Existing holistic approaches to score essays using pre-trained BERT-based models are inefficient, leading to inferior qualities in data-scarce traits. |
| Approach: | They propose an autoregressive prediction of multi-trait scores using pre-trained T5 models. |
| Outcome: | The proposed model shows over 5% improvement in prompts and traits compared to previous models . |
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. |
Can Large Language Models Automatically Score Proficiency of Written Essays? (2024.lrec-main)
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| Challenge: | Automated essay scoring (AES) is one of the earliest research problems in natural language processing. |
| Approach: | They propose to use large language models to analyze and score written essays using four different prompts. |
| Outcome: | The proposed models show comparable performance on four different prompts and a slight advantage over the state-of-the-art models. |
Rationale Behind Essay Scores: Enhancing S-LLM’s Multi-Trait Essay Scoring with Rationale Generated by LLMs (2025.findings-naacl)
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| Challenge: | Existing automated essay scoring relies on essay text without explanatory rationales for the scores. |
| Approach: | They propose a rationale-based multiple trait scoring approach that integrates large language models with a smaller large language model to generate trait-specific rationales. |
| Outcome: | The proposed approach outperforms state-of-the-art models and vanilla S-LLMs on benchmark datasets. |
ICLE++: Modeling Fine-Grained Traits for Holistic Essay Scoring (2024.naacl-long)
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| Challenge: | Recent advances in automated essay scoring have limited the generalizability of models trained on ASAP. |
| Approach: | They propose to annotate persuasive student essays with holistic and trait-specific scores in a corpus of persuasive student essay annotated with ICLE++. |
| Outcome: | The proposed model can be used to evaluate models for newer AES problems such as multi-trait scoring and cross-prompt scoring. |
FeedEval: Pedagogically Aligned Evaluation of LLM-Generated Essay Feedback (2026.findings-acl)
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| Challenge: | Recent research emphasizes the generation of high-quality feedback that provides justification and actionable guidance. |
| Approach: | They propose an LLM-based framework for evaluating LLM feedback along three dimensions: specificity, helpfulness, and validity. |
| Outcome: | The proposed framework evaluates LLM-generated feedback along three dimensions: specificity, helpfulness, and validity. |
Automated Topical Component Extraction Using Neural Network Attention Scores from Source-based Essay Scoring (2020.acl-main)
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| Challenge: | Automated essay scoring (AES) can grade essays at scale, while automated writing evaluation (AWE) does not provide useful feature representations for supporting AWE. |
| Approach: | They propose a method for linking AWE and neural AES by extracting Topical Components (TCs) representing evidence from a source text using the intermediate output of attention layers. |
| Outcome: | The proposed system is comparable to existing AWE systems for grading essays and representing essays as rubric-based features. |
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 . |
Zero-shot Cross-lingual Automated Essay Scoring (2024.lrec-main)
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| Challenge: | Existing approaches to automate essay scoring (AES) use pre-trained multilingual representations and writing quality alignment to score essays in unseen languages. |
| Approach: | They propose a novel cross-lingual scoring method using pretrained multilingual representation and writing quality alignment to represent multilingual essays. |
| Outcome: | The proposed method achieves state-of-the-art cross-lingual scoring performance. |