Neural Automated Essay Scoring Incorporating Handcrafted Features (2020.coling-main)
Copied to clipboard
| 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. |
Similar Papers
Analytic Automated Essay Scoring Based on Deep Neural Networks Integrating Multidimensional Item Response Theory (2022.coling-1)
Copied to clipboard
| Challenge: | Essay exams have two drawbacks in that grading them is expensive and raises questions about fairness. |
| Approach: | They propose to use a multidimensional item response theory model to improve interpretability while maintaining scoring accuracy. |
| Outcome: | The proposed model improves interpretability while maintaining accuracy while preserving cost and accuracy. |
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. |
Automated Topical Component Extraction Using Neural Network Attention Scores from Source-based Essay Scoring (2020.acl-main)
Copied to clipboard
| 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. |
Conundrums in Cross-Prompt Automated Essay Scoring: Making Sense of the State of the Art (2024.acl-long)
Copied to clipboard
| 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. |
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. |
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. |
TDNN: A Two-stage Deep Neural Network for Prompt-independent Automated Essay Scoring (P18-1)
Copied to clipboard
| 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. |
Automatic Essay Scoring Incorporating Rating Schema via Reinforcement Learning (D18-1)
Copied to clipboard
| Challenge: | Existing systems for automatic essay scoring are trained to predict the score of each essay at a time without considering rating schema. |
| Approach: | They propose a reinforcement learning framework that incorporates quadratic weighted kappa as guidance to optimize the scoring system. |
| Outcome: | Experiments on benchmark datasets show the proposed framework is effective. |
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. |