End-to-End Architecture for English Reading and Writing Content Assessment Based on Prompt Learning
With the acceleration of globalization, the use of English as an international language has become increasingly widespread, making the assessment of English reading and writing skills a key issue in the field of language education. However, traditional methods of English reading and writing content...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10772188/ |
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| author | Su-Qin Wu Ming-Yong Pang Xue-Mei Sun Xin-Jian Wang Yun-Peng Ji |
| author_facet | Su-Qin Wu Ming-Yong Pang Xue-Mei Sun Xin-Jian Wang Yun-Peng Ji |
| author_sort | Su-Qin Wu |
| collection | DOAJ |
| description | With the acceleration of globalization, the use of English as an international language has become increasingly widespread, making the assessment of English reading and writing skills a key issue in the field of language education. However, traditional methods of English reading and writing content assessment rely on manually designed features, which struggle to effectively handle complex and diverse language structures. To address this issue, this study proposes an English reading and writing content assessment algorithm based on Prompt Learning, utilizing an end-to-end architecture enhanced with multi-scale attention mechanisms. The algorithm preprocesses the input English texts through the prompt learning framework and uses multi-scale attention mechanisms to improve the model’s ability to capture features at different linguistic levels. Within an end-to-end architecture, the entire assessment process is automated, from text input to output of assessment results, eliminating the need for manually designed feature extraction steps. Experimental results show that the algorithm performs excellently on multiple English reading and writing assessment datasets, significantly enhancing the accuracy and efficiency of assessments and offering an effective solution for English reading and writing evaluations. |
| format | Article |
| id | doaj-art-01d2ca59b87a47dc83c89f6fd86e443e |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-01d2ca59b87a47dc83c89f6fd86e443e2025-01-16T00:01:45ZengIEEEIEEE Access2169-35362025-01-01133653366610.1109/ACCESS.2024.350999010772188End-to-End Architecture for English Reading and Writing Content Assessment Based on Prompt LearningSu-Qin Wu0Ming-Yong Pang1Xue-Mei Sun2Xin-Jian Wang3Yun-Peng Ji4https://orcid.org/0009-0004-7310-5978School of Education Science, Nanjing Normal University, Nanjing, ChinaSchool of Education Science, Nanjing Normal University, Nanjing, ChinaChuzhou Vocational and Technical College, Chuzhou, ChinaCollege of Health and Welfare, Dongshin University, Naju-si, Jeollanam-do, South KoreaSchool of Humanities, Zhujiang College, South China Agricultural University, Guangzhou, ChinaWith the acceleration of globalization, the use of English as an international language has become increasingly widespread, making the assessment of English reading and writing skills a key issue in the field of language education. However, traditional methods of English reading and writing content assessment rely on manually designed features, which struggle to effectively handle complex and diverse language structures. To address this issue, this study proposes an English reading and writing content assessment algorithm based on Prompt Learning, utilizing an end-to-end architecture enhanced with multi-scale attention mechanisms. The algorithm preprocesses the input English texts through the prompt learning framework and uses multi-scale attention mechanisms to improve the model’s ability to capture features at different linguistic levels. Within an end-to-end architecture, the entire assessment process is automated, from text input to output of assessment results, eliminating the need for manually designed feature extraction steps. Experimental results show that the algorithm performs excellently on multiple English reading and writing assessment datasets, significantly enhancing the accuracy and efficiency of assessments and offering an effective solution for English reading and writing evaluations.https://ieeexplore.ieee.org/document/10772188/Prompt learningtext assessmentmulti-scale attentionNLP educationlanguage evaluation |
| spellingShingle | Su-Qin Wu Ming-Yong Pang Xue-Mei Sun Xin-Jian Wang Yun-Peng Ji End-to-End Architecture for English Reading and Writing Content Assessment Based on Prompt Learning IEEE Access Prompt learning text assessment multi-scale attention NLP education language evaluation |
| title | End-to-End Architecture for English Reading and Writing Content Assessment Based on Prompt Learning |
| title_full | End-to-End Architecture for English Reading and Writing Content Assessment Based on Prompt Learning |
| title_fullStr | End-to-End Architecture for English Reading and Writing Content Assessment Based on Prompt Learning |
| title_full_unstemmed | End-to-End Architecture for English Reading and Writing Content Assessment Based on Prompt Learning |
| title_short | End-to-End Architecture for English Reading and Writing Content Assessment Based on Prompt Learning |
| title_sort | end to end architecture for english reading and writing content assessment based on prompt learning |
| topic | Prompt learning text assessment multi-scale attention NLP education language evaluation |
| url | https://ieeexplore.ieee.org/document/10772188/ |
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