Intelligent Auto-grading System

Abstract

Teachers tend to set the free-text questions for testing the comprehensive ability of students. That leads to the increasing attention to the intelligent auto-grading system for easing the grading load on examiners. In this paper, we present a novel automatic essay scoring system based on Natural Language Processing and Deep Learning technologies. In particular, the proposed system encodes an essay as sequential embeddings and harnesses a bi-directional LSTM to catch the semantic information. Meanwhile, the system constructs the attention for each essay so that the network can learn to focus on the valid information correctly in an article, which can also provide the reasonable evidence of the predictive result. The dataset for training and testing is the public essay set available in the Automated Student Assessment Prize on Kaggle. The study shows that our system achieves state-of-the-art performance in grade prediction, and more importantly, our intelligent auto-grading system can focus on the critical words and sentences, analyze the logical semantic relationship of the context and predict the interpretable grades.