Prompt-based Content Scoring for Automated Spoken Language Assessment
نویسندگان
چکیده
This paper investigates the use of promptbased content features for the automated assessment of spontaneous speech in a spoken language proficiency assessment. The results show that single highest performing promptbased content feature measures the number of unique lexical types that overlap with the listening materials and are not contained in either the reading materials or a sample response, with a correlation of r = 0.450 with holistic proficiency scores provided by humans. Furthermore, linear regression scoring models that combine the proposed promptbased content features with additional spoken language proficiency features are shown to achieve competitive performance with scoring models using content features based on prescored responses.
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