Assessment of Indonesian Short Essay using Transfer Learning Siamese Dependency Tree-LSTM

Agung Wiratmo, Chastine Fatichah

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Citations (Scopus)

Abstract

The automatic short essay assessment is considering many aspects, not only the student's answer, but also the teacher marking rubrics for getting precise answers. Sometimes the marking rubrics are not used, instead, the teacher only used a single precise answer so that condition raises scoring ambiguity. This paper presents some precise answers for a fair assessment and discovers the important representation of the answer using grammatical dependency. Grammatical used to overcome uncertainty relation word in answer. The precise answers as the correct answer tolerance and student answers are used as the new input pair. Due to the Indonesian short essay is a lack, transfer learning is carried out. In this paper, we present a transfer learning using Siamese dependency tree-LSTM for obtaining the assessment score. The evaluation of the proposed method using Quadratic Weighted Kappa and accuracy that is 85.74% and 70.80%, respectively.

Original languageEnglish
Title of host publicationICICoS 2020 - Proceeding
Subtitle of host publication4th International Conference on Informatics and Computational Sciences
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728195261
DOIs
Publication statusPublished - 10 Nov 2020
Event4th International Conference on Informatics and Computational Sciences, ICICoS 2020 - Semarang, Indonesia
Duration: 10 Nov 202011 Nov 2020

Publication series

NameICICoS 2020 - Proceeding: 4th International Conference on Informatics and Computational Sciences

Conference

Conference4th International Conference on Informatics and Computational Sciences, ICICoS 2020
Country/TerritoryIndonesia
CitySemarang
Period10/11/2011/11/20

Keywords

  • Automatic assessment
  • short essay assessment
  • siamese dependency tree-LSTM
  • transfer learning

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