Automated Essay Scoring System for Nonnative Japanese Learners

Reo Hirao, Mio Arai, Hiroki Shimanaka, Satoru Katsumata, Mamoru Komachi


Abstract
In this study, we created an automated essay scoring (AES) system for nonnative Japanese learners using an essay dataset with annotations for a holistic score and multiple trait scores, including content, organization, and language scores. In particular, we developed AES systems using two different approaches: a feature-based approach and a neural-network-based approach. In the former approach, we used Japanese-specific linguistic features, including character-type features such as “kanji” and “hiragana.” In the latter approach, we used two models: a long short-term memory (LSTM) model (Hochreiter and Schmidhuber, 1997) and a bidirectional encoder representations from transformers (BERT) model (Devlin et al., 2019), which achieved the highest accuracy in various natural language processing tasks in 2018. Overall, the BERT model achieved the best root mean squared error and quadratic weighted kappa scores. In addition, we analyzed the robustness of the outputs of the BERT model. We have released and shared this system to facilitate further research on AES for Japanese as a second language learners.
Anthology ID:
2020.lrec-1.157
Volume:
Proceedings of the 12th Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Venues:
COLING | LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
1250–1257
Language:
English
URL:
https://www.aclweb.org/anthology/2020.lrec-1.157
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.lrec-1.157.pdf