Automated Scoring Research over 40 Years: Looking Back and Ahead

Jiang, Jinlin; Wei, Wei

doi:10.3923/jai.2012.56.63

Cited by 4 publications

(1 citation statement)

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“…First, it only used 300 translated texts of a narration to build scoring models, while different types of texts have remarkable differences in content and language, so it is hard to determine whether the quality predictors will be effective for other text types. Second, the study used a hold-out method, by which the training translations were only used for modeling and the validation translations were only utilized to test the models, so the results may be different if they switch roles [15].…”

Section: Introductionmentioning

confidence: 99%

Constructing Automated Scoring Model for Human Translation with Multidisciplinary Technologies

Jiang¹,

Qin²,

Ya³

2016

Int. J. Emerg. Technol. Learn.

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A Ab bs st tr ra ac ct t--This study developed 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 largescale examinations. This study built five 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 finalized model can produce reliable scores for Chinese EFL learners' English-toChinese expository translations.

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Section: Introductionmentioning

confidence: 99%