Design of an intelligent grading system for college English translation based on big data technology

Background: The Intelligent Grading System (IGS), now in use for college English translation, has the following problems in actual use: the running time is excessive, and the final score result differs significantly from the actual one. Given this scenario, it is essential to substantially increase grading efficiency and result correctness to reduce manual participation in the enhancing grading efficiency. Objective: This research investigates the use of big data technology in designing an IGS for college English translation. The study focuses on the intersection of literature and English language teaching, aiming to enhance the accuracy and efficiency of grading translation assignments. Methods: The deep learning methodology is the core approach for developing the intelligent grading system. By leveraging the power of a Hybrid gradient-boosting decision tree with an ensemble Back Propagation Neural Network (HGBDT-EBPNN), the system learns from large volumes of labeled translation data to identify patterns and extract meaningful features that contribute to accurate grading. Results: The findings of this research contribute to the growing body of knowledge on the use of big data technology and deep learning in the field of translation assessment. The proposed study has provided 98 % of accuracy in the performance metrics. Conclusion: The IGS offers a promising solution for enhancing the efficiency and objectivity of grading college English translation assignments. It could improve the quality of feedback provided to students as well as streamline the assessment process for instructors.