Automatic Short Answer Grading using Siamese Bidirectional LSTM Based Regression

Automatic student assessment plays an important role in education - it provides instant feedback to learners, and at the same time reduces tedious grading workload for instructors. In this paper, we investigate new machine learning techniques for automatic short answer grading (ASAG). The ASAG problem mainly involves assessing short, natural language responses to given questions automatically. While current research in the field has focused either on feature engineering or deep learning, we propose a new approach which combines the advantages of both. More specifically, we propose a Siamese Bidirectional LSTM Neural Network based Regressor in conjunction with handcrafted features for ASAG. Extensive experiments using the popular Mohler ASAG dataset which contains training samples from Computer Science courses, have demonstrated that our system, despite being simpler, provides similar or better overall performance in terms of grading accuracy (measured with Pearson r, mean absolute error and root mean squared error) compared to state-of-the-art results.