Syntactic Graph Co-Attention Network for Automatic Short Answer Grading

In this work, we addressed the problem of Automatic Short Answer Grading (ASAG). The task involves assigning a grade to a student’s answer by comparing it against a model answer for a given question. Previous works in this domain mostly used rule-based and machine learning methods to tackle the problem, wherein the creation of handcrafted features and the use of neural networks have been the most common practice. Different variations of syntactic and semantic similarity between a student and model answer pair have been used as features in earlier works. We hypothesize that the extent of alignment between the graph representations of a student and model answer is a good indicator of their relative similarity. In this direction, we propose an end-to-end ASAG system that models the alignment as co-attention between the nodes in the dependency graphs corresponding to an answer pair. We leveraged the representational power of BERT and Graph Convolutional Network (GCN) along with a co-attention mechanism, to capture the intrinsic similarities between student answers and reference answers. Our proposed method surpasses most of the existing state-of-the-art results on the SemEval-2013 SciEntsBank and BEETLE datasets.