Abstract
Automatic Evaluation of Text Answers, popularly known as Automatic Short Answer Grading (ASAG) is an area of research and development currently. The widespread acceptance of online learning and increased number of enrolments in such courses has necessitated the creation of a method that can be applied across platforms for all types of supply based answers. The current paper proposes a simple technique using the Deep Learning Based Universal Sentence Encoder to generate vectors for each answer. These vectors can then be compared against vectors generated from model answers to get the final score for the student’s answer. Experimental results show that for a sizeable dataset, the approach works well and can be considered a reliable approach.
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Chakraborty, C., Sethi, R., Chauhan, V., Sarma, B., Chakraborty, U.K. (2023). Automatic Short Answer Grading Using Universal Sentence Encoder. In: Auer, M.E., Pachatz, W., Rüütmann, T. (eds) Learning in the Age of Digital and Green Transition. ICL 2022. Lecture Notes in Networks and Systems, vol 633. Springer, Cham. https://doi.org/10.1007/978-3-031-26876-2_49
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DOI: https://doi.org/10.1007/978-3-031-26876-2_49
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