  Automatic short answer grading (ASAG), which autonomously score student
answers according to reference answers, provides a cost-effective and
consistent approach to teaching professionals and can reduce their monotonous
and tedious grading workloads. However, ASAG is a very challenging task due to
two reasons: (1) student answers are made up of free text which requires a deep
semantic understanding; and (2) the questions are usually open-ended and across
many domains in K-12 scenarios. In this paper, we propose a generalized
end-to-end ASAG learning framework which aims to (1) autonomously extract
linguistic information from both student and reference answers; and (2)
accurately model the semantic relations between free-text student and reference
answers in open-ended domain. The proposed ASAG model is evaluated on a large
real-world K-12 dataset and can outperform the state-of-the-art baselines in
terms of various evaluation metrics.
