Neural-based automatic scoring model for Chinese-English interpretation with a multi-indicator assessment

Abstract

Manual evaluation could be time-consuming, unreliable and unreproducible in Chinese-English interpretation. Therefore, it is necessary to develop an automatic scoring system. This paper proposes an accurate automatic scoring model for Chinese-English interpretation via a multi-indicator assessment. From the three dimensions (i.e. keywords, content, and grammar) of the scoring rubrics, three improved attention-based BiLSTM neural models are proposed to learn the text of the transcribed responses. In the feature vectorisation stage, the pre-training model Bert is utilised to vectorise the keywords and content, and a random initialisation is used for the grammar. In addition, the fluency is also taken into account based on the speech speed. The overall holistic score is obtained by fusing the four scores using the random forest regressor. The experimental results demonstrate that the proposed scoring method is effective and can perform as good as the manual scoring.