Coherence‐based automatic short answer scoring using sentence embedding

This paper proposes an automated essay scoring system using sentence-BERT embeddings and Bi-LSTM networks to evaluate student short-answer responses, focusing on capturing coherence and cohesion in essays. The system is tested on the ASAP Kaggle dataset and a domain-specific dataset with 2,500 student responses, achieving an average QWK score of 0.76.

Automatic essay scoring (AES) is an essential educational application in natural language processing. This automated process will alleviate the burden by increasing the reliability and consistency of the assessment. With the advances in text embedding libraries and neural network models, AES systems achieved good results in terms of accuracy. However, the actual goals still need to be attained, like embedding essays into vectors with cohesion and coherence, and providing student feedback is stil

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