Triplet Loss based Siamese Networks for Automatic Short Answer Grading

Abstract

Grading student work is critical for assessing their understanding and providing necessary feedback. However, answer grading can become monotonous for teachers. On the standard ASAG data set, our system shows substantial improvements in classification disparity of correct and incorrect answers from a reference answer compared to baseline methods. Our supervised model (1) utilizes recent advances in semantic word embeddings and (2) implements ideas from one-shot learning methods, which are proven to work with minimal. We present experimental results from a model based on different approaches and demonstrates decent performance on standard benchmark dataset.