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Automated Essay Scoring Using Recurrence over BERT (RoBERT)Automated Essay Scoring Using Recurrence over BERT (RoBERT)

Other Titles
Automated Essay Scoring Using Recurrence over BERT (RoBERT)
Authors
이인구남호성
Issue Date
2021
Publisher
한국응용언어학회
Keywords
automated essay scoring; trait-specific essay scoring; essay evaluation; Recurrence over BERT (RoBERT); hierarchical transformers
Citation
응용언어학, v.37, no.3, pp 7 - 28
Pages
22
Indexed
KCI
Journal Title
응용언어학
Volume
37
Number
3
Start Page
7
End Page
28
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/138176
ISSN
1225-3871
2765-3773
Abstract
This study aimed to build a system that could automate students' English essay evaluation by using Recurrence over BERT (RoBERT), a state-of-the-art deep learning model. English essay evaluation is inherently time-consuming. It may reflect teacher bias. English teachers are usually burdened with the task of evaluating many essays in a short period of time. Automated essay scoring (AES) can solve these problems. It has the advantage of being able to evaluate essays in a short time and without bias. In this paper, the RoBERT model was trained and evaluated on Essay Set #8 of the Automated Student Assessment Prize (ASAP) dataset. The 5-fold cross validation evaluation method was used for fair comparison with the previously suggested AES models. As a result, the RoBERT model showed the highest agreement with the human raters’ resolved scores in 5 out of 6 trait scores than the previous evaluation models. The advantage of it is that it can use the pre-trained BERT model and deal with long inputs, overcoming the input size limit of the BERT model. It was confirmed that the RoBERT model works well for trait-specific evaluation of long essays. Thus, the RoBERT model can be used as an auxiliary means to automate the evaluation of students' essays and reduce the excessive work of English teachers.
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