On the Use of Bert for Automated Essay Scoring: Joint Learning of Multi-Scale Essay Representation (2022.naacl-main)
Copied to clipboard
| Challenge: | Pre-trained models have not been used to outperform other deep learning models such as CNN in Automated Essay Scoring (AES). |
| Approach: | They propose a novel multi-scale essay representation for BERT that can be jointly learned . they employ multiple losses and transfer learning from out-of-domain essays to further improve performance . |
| Outcome: | The proposed model outperforms existing models in the area of automated essay scoring . the proposed model generalizes well to the CommonLit Readability Prize data set . |
Similar Papers
Enhancing Automated Essay Scoring Performance via Fine-tuning Pre-trained Language Models with Combination of Regression and Ranking (2020.findings-emnlp)
Copied to clipboard
| 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. |
Beyond Canonical Fine-tuning: Leveraging Hybrid Multi-Layer Pooled Representations of BERT for Automated Essay Scoring (2024.lrec-main)
Copied to clipboard
| 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. |
Autoregressive Score Generation for Multi-trait Essay Scoring (2024.findings-eacl)
Copied to clipboard
| 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 . |
Multi-task Learning for Automated Essay Scoring with Sentiment Analysis (2020.aacl-srw)
Copied to clipboard
| 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. |
EssayJudge: A Multi-Granular Benchmark for Assessing Automated Essay Scoring Capabilities of Multimodal Large Language Models (2025.findings-acl)
Copied to clipboard
Jiamin Su, Yibo Yan, Fangteng Fu, Zhang Han, Jingheng Ye, Xiang Liu, Jiahao Huo, Huiyu Zhou, Xuming Hu
| Challenge: | Automated Essay Scoring (AES) systems face three major challenges: reliance on handcrafted features that limit generalizability, difficulty in capturing fine-grained traits like coherence and argumentation, and inability to handle multimodal contexts. |
| Approach: | They propose a multimodal benchmark to evaluate AES capabilities across lexical-, sentence-, and discourse-level traits without manual feature engineering. |
| Outcome: | The proposed system can evaluate AES capabilities across lexical-, sentence-, and discourse-level traits without manual feature engineering. |
Automated Essay Scoring: A Reflection on the State of the Art (2024.emnlp-main)
Copied to clipboard
| 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 . |
Automated Essay Scoring System for Nonnative Japanese Learners (2020.lrec-1)
Copied to clipboard
| 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. |
Automated Essay Scoring via Pairwise Contrastive Regression (2022.coling-1)
Copied to clipboard
| Challenge: | Existing approaches to automate essay scoring use regression or ranking objectives . a novel neural pairwise ranking model is developed to optimize both objectives based on the same loss . |
| Approach: | They propose a novel Neural Pairwise Contrastive Regression model that optimizes both objectives simultaneously as a single loss. |
| Outcome: | The proposed model outperforms previous methods on the public Automated Student Assessment Prize dataset. |
Beyond the Gold Standard in Analytic Automated Essay Scoring (2025.acl-srw)
Copied to clipboard
| 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. |
Neural Automated Essay Scoring and Coherence Modeling for Adversarially Crafted Input (N18-1)
Copied to clipboard
| 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. |