Automated Essay Scoring
26 papers with code • 1 benchmarks • 1 datasets
Essay scoring: Automated Essay Scoring is the task of assigning a score to an essay, usually in the context of assessing the language ability of a language learner. The quality of an essay is affected by the following four primary dimensions: topic relevance, organization and coherence, word usage and sentence complexity, and grammar and mechanics.
Source: A Joint Model for Multimodal Document Quality Assessment
Benchmarks
These leaderboards are used to track progress in Automated Essay Scoring
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ASAP
Latest papers with no code
The Effectiveness of a Dynamic Loss Function in Neural Network Based Automated Essay Scoring
Neural networks and in particular the attention mechanism have brought significant advances to the field of Automated Essay Scoring.
Can ChatGPT and Bard Generate Aligned Assessment Items? A Reliability Analysis against Human Performance
ChatGPT and Bard are AI chatbots based on Large Language Models (LLM) that are slated to promise different applications in diverse areas.
Using Active Learning Methods to Strategically Select Essays for Automated Scoring
All three active learning methods produced strong results, with the topological-based method producing the most efficient classification.
Data Augmentation for Automated Essay Scoring using Transformer Models
It has been explored for a number of years, and it remains partially solved.
Toward Educator-focused Automated Scoring Systems for Reading and Writing
This paper presents methods for improving automated essay scoring with techniques that address the computational trade-offs of self-attention and document length.
The effects of data size on Automated Essay Scoring engines
We study the effects of data size and quality on the performance on Automated Essay Scoring (AES) engines that are designed in accordance with three different paradigms; A frequency and hand-crafted feature-based model, a recurrent neural network model, and a pretrained transformer-based language model that is fine-tuned for classification.
Automated essay scoring using efficient transformer-based language models
Automated Essay Scoring (AES) is a cross-disciplinary effort involving Education, Linguistics, and Natural Language Processing (NLP).
Multi-task Learning for Automated Essay Scoring with Sentiment Analysis
Multi-task learning models, one of the deep learning techniques that have recently been applied to many NLP tasks, demonstrate the vast potential for AES.
Neural Automated Essay Scoring Incorporating Handcrafted Features
One of the most popular hybrid methods is formulated as a DNN-AES model with an additional recurrent neural network (RNN) that processes a sequence of handcrafted sentence-level features.
Enhancing Automated Essay Scoring Performance via Fine-tuning Pre-trained Language Models with Combination of Regression and Ranking
However, to solve the AES task, previous works utilize shallow neural networks to learn essay representations and constrain calculated scores with regression loss or ranking loss, respectively.
