An English Translation Error Correction System Based on Multi-Feature Fusion

Abstract

Multi-feature fusion is an advanced computational technique that combines multiple sources of information or characteristics to improve the performance of a system or model. In various fields such as computer vision, natural language processing, and machine learning, this approach integrates diverse features like visual cues, textual data, and contextual information to enhance accuracy, robustness, and efficiency. Multi-feature fusion-based English translation represents a cutting-edge approach in natural language processing, where diverse linguistic features are amalgamated to refine the accuracy and quality of translation outcomes. By integrating various elements such as syntax, semantics, context, and linguistic patterns, this method significantly enhances the translation process, ensuring more nuanced and contextually precise results. This paper introduces an innovative English Translation Error Correction System (ETECS) founded on Multi-Feature Fusion, supplemented by a bi-gram Statistical Classification Neural Network (n-gramCNN). In response to the growing demand for accurate and efficient translation error detection and correction, this research endeavors to enhance the quality of English translations through a comprehensive and advanced methodology. Using a dataset of 1,000 English text samples with corresponding translations, ETECS demonstrates an impressive error detection accuracy of 95%. Upon identifying errors, the system achieves a correction accuracy of 92%, effectively rectifying grammatical, syntactical, and semantic inconsistencies. Moreover, ETECS outperforms baseline error correction systems by a significant margin, showcasing its superiority in handling complex translation errors. The bi-gram Statistical Classification Neural Network (n-gramCNN) contributes to this success by achieving an F1 score of 0.94, highlighting its effectiveness in classifying and correcting translation errors.