Deep learning for Short Answer Scoring
Surya K1, Ekansh Gayakwad2, Nallakaruppan, M.K3

1Surya K, Vellore Institute of Technology, Vellore, (Tamil Nadu), India.
2Ekansh Gayakwad, Vellore Institute of Technology, Vellore, (Tamil Nadu), India.
3Nallakaruppan M.K, Vellore Institute of Technology, Vellore, (Tamil Nadu), India.

Manuscript received on 23 March 2019 | Revised Manuscript received on 30 March 2019 | Manuscript published on 30 March 2019 | PP: 1712-1715 | Volume-7 Issue-6, March 2019 | Retrieval Number: F2253037619/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Automated scoring of descriptive answers can find applications in educational assessment and is one of the applications of Natural Language Processing. Deep learning has contributed significantly to the growth of NLP in recent years. Deep NLP techniques are ideal for automated scoring especially short answer scoring tasks. We compare some common deep learning models for the SAS task.
Keywords: NLP,DLP, SAS task.

Scope of the Article: Deep learning