Evaluating the applicability of the transformer-based grammatical error correction system for assessing language accuracy

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Publicat a:Language Testing in Asia vol. 15, no. 1 (Dec 2025), p. 75
Autor principal: Hwang, Haerim
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Springer Nature B.V.
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100 1 |a Hwang, Haerim  |u Chinese University of Hong Kong, Hong Kong, China (GRID:grid.10784.3a) (ISNI:0000 0004 1937 0482) 
245 1 |a Evaluating the applicability of the transformer-based grammatical error correction system for assessing language accuracy 
260 |b Springer Nature B.V.  |c Dec 2025 
513 |a Journal Article 
520 3 |a The current study focuses on the use of Grammatical Error Correction (GEC) technology for assessing language accuracy, which has received relatively less attention than complexity and fluency in the context of automated assessment. Adopting a technology-enhanced approach to language assessment, rather than a technology-driven approach, we critically assessed the suitability of the state-of-the-art GEC system for assessing language accuracy in Korean, an understudied language in this regard. We analyzed how reliable this system is quantitatively and what types of error can be generated by this system qualitatively. Also, we used out-of-domain, inclusive data from heritage speakers of Korean, which has never been considered in the development of GEC. Our accuracy analyses show that the system achieves a fairly high accuracy in differentiating between correct and incorrect sentences on our data (F0.5 = 0.819). However, the system exhibits a tendency to make unnecessary corrections, such as inserting topics/adverbials or correcting particles, while failing to correct ungrammatical ones in some cases. These findings from our mixed-method analyses suggest that language evaluators should recognize the potential for inaccurate assessments when using a GEC system, as its output may be incorrect at this moment, thus highlighting the critical need for digital language assessment literacy. 
653 |a Error analysis 
653 |a Politics 
653 |a Language 
653 |a Technology 
653 |a Function words 
653 |a Error correction & detection 
653 |a Fundamental frequency 
653 |a Evaluation 
653 |a Korean language 
653 |a Accuracy 
653 |a Literacy 
653 |a Deep learning 
653 |a Fluency 
653 |a Suitability 
653 |a Social networks 
653 |a Automation 
653 |a Language assessment 
653 |a Natural language 
653 |a Heritage language 
653 |a Cultural heritage 
653 |a Second language learning 
653 |a Large language models 
653 |a Adverbials 
653 |a Oral Language 
653 |a Error Correction 
653 |a Natural Language Processing 
653 |a Educational Objectives 
653 |a Error Analysis (Language) 
653 |a Language Processing 
653 |a Grammar 
653 |a Recall (Psychology) 
653 |a Language Usage 
653 |a Korean 
653 |a Diaries 
653 |a Influence of Technology 
653 |a Periodicals 
653 |a Error Patterns 
653 |a Sentences 
653 |a Native Speakers 
653 |a Evaluators 
773 0 |t Language Testing in Asia  |g vol. 15, no. 1 (Dec 2025), p. 75 
786 0 |d ProQuest  |t Education Database 
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