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        <title>Automated Essay Scoring System for Nonnative Japanese Learners</title>
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    <name type="personal">
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    <name type="personal">
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    <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.</abstract>
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%0 Conference Proceedings
%T Automated Essay Scoring System for Nonnative Japanese Learners
%A Hirao, Reo
%A Arai, Mio
%A Shimanaka, Hiroki
%A Katsumata, Satoru
%A Komachi, Mamoru
%S Proceedings of the Twelfth Language Resources and Evaluation Conference
%D 2020
%8 May
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-34-4
%G English
%F hirao-etal-2020-automated
%X 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.
%U https://aclanthology.org/2020.lrec-1.157
%P 1250-1257

Markdown (Informal)

[Automated Essay Scoring System for Nonnative Japanese Learners](https://aclanthology.org/2020.lrec-1.157) (Hirao et al., LREC 2020)

• Automated Essay Scoring System for Nonnative Japanese Learners (Hirao et al., LREC 2020)

ACL

• Reo Hirao, Mio Arai, Hiroki Shimanaka, Satoru Katsumata, and Mamoru Komachi. 2020. Automated Essay Scoring System for Nonnative Japanese Learners. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 1250–1257, Marseille, France. European Language Resources Association.