Multi-region English translation synchronization mechanism driven by big data

Xu, Junrui

doi:10.1007/s12065-022-00779-y

Cited by 2 publications

(1 citation statement)

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“…Similarly, in natural language processing, combining syntactic, semantic, and contextual features can lead to more robust language understanding and generation models [3]. The key challenge in multi-feature fusion lies in effectively integrating diverse sources of information while avoiding redundancy and preserving the most relevant aspects of each feature [4]. Techniques such as feature weighting, feature selection, and deep learning architectures like multi-modal networks are commonly employed to address these challenges and achieve optimal fusion results [5].…”

Section: Introductionmentioning

confidence: 99%

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

Liu

2024

jes

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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.

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Section: Introductionmentioning

confidence: 99%