Automatic short answer grading (ASAG) is the task of automatically grading students answers which are a few words to a few sentences long. While supervised machine learning techniques (classification, regression) have been successfully applied for ASAG, they suffer from the constant need of instructor graded answers as labelled data. In this paper, we propose a transfer learning based technique for ASAG built on an ensemble of text classifier of student answers and a classifier using numeric features derived from various similarity measures with respect to instructor provided model answers. We present preliminary empirical results to demonstrate efficacy of the proposed technique.
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