Constructing Automated Scoring Model for Human Translation with Multidisciplinary Technologies

Jinlin Jiang, Ying Qin, Ya Sun


This study constructed a computer scoring model for Chinese EFL learners’ English-to-Chinese translations using multidisciplinary techniques in corpus linguistics, natural language processing, information retrieval and statistics. The proposed model, once implemented as computer software, can score English-to-Chinese translations in large-scale examinations. This study built five tentative scoring models with 50, 100, 130, 150 and 180 translations as the training set for 300 translations of an expository writing. The correlation coefficients between the computed scores of these models and human-assigned scores were above 0.8. The results further indicated that the computed scores with 130 training translations were closest to human-assigned scores. Therefore, it was concluded that the text features extracted in this research were effective and the finalized model can produce reliable scores for Chinese EFL learners’ English-to-Chinese expository translations.


Automated scoring; English-to-Chinese translation; Multidisciplinary technologies; Text features

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Copyright (c) 2017 Jinlin Jiang, Ying Qin, Ya Sun

International Journal of Emerging Technologies in Learning. ISSN: 1863-0383
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